Accurate IP geolocation is indispensable for location-aware applications. While recent advances based on router-centric IP graphs are considered cutting-edge, one challenge remain: the prevalence of sparse IP graphs (14.24% with fewer than 10 nodes, 9.73% isolated) limits graph learning. To mitigate this issue, we designate the target host as the central node and aggregate multiple last-hop routers to construct the target-centric IP graph, instead of relying solely on the router with the smallest last-hop latency as in previous works. Experiments on three real-world datasets show that our method significantly improves the geolocation accuracy compared to existing baselines.

The elevation data obtained from radar altimetry can assist interferometric synthetic aperture radar (InSAR) technology in achieving more accurate absolute height calibration in generating digital elevation model (DEM). The traditional height calibration method determines the calibration constant by comparing the InSAR DEM with the reference DEM after InSAR generates the DEM. This paper proposes a calibration method during the phase signal processing that not only accomplishes height calibration but also helps InSAR break the phase continuity assumption using radar altimetry to obtain accurate absolute phase, thereby improving DEM product quality. Experiments conducted in the southern region of Greenland demonstrate that the integration of radar altimetry data facilitates the seamless combination of InSAR phase unwrapping and height calibration.

The spatial variation of forest aboveground carbon sequestration (ACS) is crucial for assessing the carbon storage and time of carbon-emission peaking. Unfortunately, a systematic assessment of accurate ACS and its change has not yet been developed, especially at a large scale. Here, we evaluate the spatiotemporal ACS trend based on LANDIS PRO and PnET-II models from 2000 to 2020 in Western Sichuan, China, coupled with multi-source remote sensing datasets (e.g., MODIS and Lansat) and in situ forest inventory to obtain the landscape information. The Mann-Kendall trend test is used to examine the ACS trend. The total ACS increased from 344.62 to 466.99 Tg due to large-scale reforestation programs from 2000 to 2020. The evergreen coniferous forest contributed the most ACS (85.28%) in Western Sichuan compared with other species. The developed methodology can be applied to analyzing the ACS at the watershed and regional levels promptly and helping decision-makers and managers develop effective forest management measures.

Utilizing simulated P-band PolInSAR backscatter data in pine forests, we qualitatively and quantitatively analyzed the applicability of the RVoG model in tree height inversion. Small radar incidence angles and low forest stand densities can decrease canopy continuity, invalidating a critical RVoG model assumption. Also, the ground backscatter decreases at a large radar incidence angle, reducing the accuracy of ground phase estimation. A high stand density can ensure good canopy continuity and stable tree height inversion. Thus, for better inversion, the radar incidence angle should be around 35°, and the stand density should be ≥ 300 stems/ha.

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) possesses unique advantages in forest parameter retrieval due to its all-weather, all-day observation capability and effective acquisition of vertical structure information of ground targets. Based on the Random Volume over Ground (RVoG) model, the existing three-stage method fits the coherent line to estimate the ground phase. However, the accuracy of tree height inversion is restricted by noise during the estimation process. A new ground phase estimation method was proposed by fitting coherent lines using multiple pixels, improving the SNR. Results demonstrate that, compared to the existing three-stage algorithm, our method performs better in the tree height inversion of managed and natural forests.

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) is a combination of polarimetric SAR and interferometric SAR, possessing both the sensitivity of interferometric SAR to the vertical information of objects on the ground and the sensitivity of polarimetric SAR to the geometric morphology and dielectric constant of objects. Therefore, it is a crucial technology for inverting forest structural information.Currently, the mainstream approach for forest tree height inversion continues to explore the role of polarimetric information in the inversion process. However, the important role of interferometric SAR in this process is often overlooked. This paper innovatively proposes the estimation of the tree height gradient maximum likelihood function. It introduces a Maximum Likelihood approach for Tree-height Estimation (ML-TE) that directly extracts tree height information from interferograms. Experimental results demonstrate the effectiveness of this method in tree height inversion.

Clouds greatly affect the quality of optical remote-sensing image data. Algorithms for detecting and removing clouds can significantly enhance the utilization of optical data. Numerous studies highlight the crucial role of cirrus bands in cloud detection and removal, although only a few satellites possess this capability. In this research, a unified theory based on physical model is proposed and validated for thin cloud detection and removal. First, top of reflectance (TOA) values of thin clouds are detected in a specific band. Subsequently, the detected cloud image is used to remove thin clouds from other bands via spatial transformation. Experiments with actual Landsat-9 Operational Land Imager 2 (OLI-2) data confirm the effectiveness of the proposed approach both qualitatively and quantitatively. Even if thin clouds are undetectable in the cirrus bands, or cirrus bands are unavailable, this research still presents a novel paradigm for detecting and removing thin clouds.

The impact of cross-polarized (i.e.,${S_{HH}}{S^{\ast}}_{HV},{S_{VV}}{S^{\ast}}_{HV},{\text{ }}{S_{HV}}{S^{\ast}}_{HH},{\text{ }}and{\text{ }}{S_{HV}}{S^{\ast}}_{VV}$) backscatter on analyzing scattering mechanisms in PolSAR datasets of radar targets from forested and oriented urban areas was studied. A new decomposition algorithm was proposed by considering all nine elements in the covariance matrix. It was eigenvalue/eigenvector-based. Its validity and effectiveness in separating radar targets from forested and oriented urban areas were assessed with the Radarsat-2 C-band PolSAR backscatter data. The study area is San Francisco, California, USA. Satisfactory separation results are achieved. Compared with the three existing decomposition algorithms, the proposed algorithm should be most capable of distinguishing radar targets from forested and oriented urban areas and help improve the forest and urban parameter inversion using PolSAR backscatter data.

Adaptive multilooking applied to multiple synthetic aperture radar (SAR) observations has been proven to be an effective process to improve the quality of multitemporal interferometric SAR (InSAR), in which the key task is to select the statistically homogeneous pixels (SHPs). The existing algorithms are mainly based on time-series information from the same position and perform unsatisfactorily when image number is small. In this study, we propose an adaptive multilooking approach based on the context covariance matrix for SHP selection, named CCM-SHPS. The core idea is to exploit spatially adjacent pixels to enhance the information volume. The context covariance matrix is constructed and the Wishart statistic test is employed to measure the similarity. The proposed CCM-SHPS is validated by a simulated stack on the filtered InSAR results, including the amplitude, interferometric phase, and coherence, demonstrating its advantage over five representative algorithms in speckle suppression and edge preservation.

Phase linking is a key step in distributed scatterer interferometry (DSI), which can significantly reduce decorrelation by retrieving a consistent phase series. The performance of phase linking can be severely degraded by the inaccurate coherence magnitude matrix. Recently, some studies proposed to mitigate the problem by employing the regularization methods, e.g., adding a quantity to the diagonal or shrinking to the identity matrix. However, the correction is insufficient due to the simple structure assumption. In this study, we propose a new phase linking approach based on a powerful regularization method. Specifically, it achieves the maximum likelihood estimation of the coherence matrix under the structural constraint of total positivity. A simulated stack is exploited to test the performance of the proposed approach. The qualitative and quantitative evaluations demonstrate its superiority over the existing regularization methods.

An airport is a critical infrastructure in disaster relief, and a strong earthquake inevitably affects an airport near the epicenter. The repair of quake-induced airport damage and the airport surface deformation caused by the quake still needs to be understood to maintain its safe operation. In this study, the differential interferometric synthetic aperture radar (D-InSAR) technique was used to assess the surface deformation of Jiuzhai-Huanglong Airport and Chengdu Shuangliu International Airport with PALSAR data after the 2008 Wenchuan quake (Mw= 7.9) of China. The earthquake affected both airports. The influence on the surface deformation of Jiuzhai-Huanglong Airport is greater, although it is farther from the epicenter. The causeresult is closely related to the geological environments (e.g., setting and structure in strata) of the location and the earthwork in the airport construction process.

Due to the rugged terrain and high seismic activities in the Qinghai-Tibet Plateau region in western China, highway and railroad track construction is challenging. The airport is alternatively a critical transportation infrastructure. To create a flat area large enough for an airport, one has to fill/remove a significant amount of earth materials. The frequency and intensity of earthquakes are high in this area as well. Thus, it is significant to study the different effects of seismic activities on a high-filling/removing airport compared to nearly a non-filling/removing airport. With the small baseline subset (SBAS) InSAR technique and multi-temporal Sentinel-1 SAR data, we studied the impact of the 2017 Jiuzhaigou earthquake (Mw= 7.0) on the surface deformation near two airport areas. One (i.e., Jiuzhai-Huanglong Airport) is high-filling/removing, and the other (Aba-Hongyuan Airport) is nearly nonfilling/removing. Qualitative and quantitative comparative analyses show that seismic activities greatly influence the high-filling/removing earth materials airport.

Rice-crop intensity is the annual number of rice growth cycles in a field. Monitoring the intensity on a large scale is vital in evaluating grain production and its ecological impact. Synthetic Aperture Radar (SAR) has an all-weather imaging capability. However, the existing SAR-based rice-crop intensity mapping methods mostly focus on small regions due to the diversity of rice backscatter patterns, the inefficiency of the time-series feature extraction, and the unavailability of rice phenological information on a large scale. In this study, a harmonic-based method is proposed to identify the essential backscatter periodicities. It also suppresses short-term disturbance in time-series Sentinel-1 SAR data without setting filtering windows or assuming profile shapes. The method detects backscatter troughs, eliminating the requirement for point-by-point traversal mathematical operations. Annual temperature profiles are derived from time-series ERA5-Land data to identify troughs related to rice growth cycles under various agro-climatic conditions. Then, the single (135,537 km2), double (19,036 km2), and triple (259 km2) rice-crop intensities covering the entire Southern China in 2020 are mapped in a 10m resolution, without relying on region-specific prior phenological information. The method achieves an overall accuracy of 82.26%, and can potentially support the continental or global mapping task.

Urban residents' daily lives and commerce depend on the reliable water and sewer pipeline network. Leaking the network is a nuisance, wasting precocious resources and money. Detecting and mitigating a leak in the network is essential for utility companies and the general public. As an ongoing study, we developed a U-net-based algorithm for leak detection and explored the possibility of understanding operations within the algorithm. As dimensionality reduction and feature extraction are the primary objectives of the convoluting and pooling processes in the algorithm, the processes can be carried out by principal components analysis (PCA). The algorithm then advances a leak/non-leak classification after extracting features. The support vector machine (SVM), one machine learning algorithm, can replace and perform the classification procedure. Thus, a (PCA+SVM) algorithm is developed, and more importantly, we interpret the studied U-net-based algorithm as a hybrid of the PCA and SVM. Finally, the (PCA+SVM) algorithm is evaluated in the leaking detection, and it satisfactorily detects a leak or non-leak location in urban areas of Tianjin, China.

Low-altitude unmanned aerial vehicle (UAV) remote sensing facilitates the frequent detection of power lines and liberates manual inspection. In the process of UAV power line inspection, power line detection in UAV aerial images plays an important role. But false alarms and miss alarms often occur during the detection process. Aiming at this problem, a power line detection method based on Maxtree is proposed. This method transforms the UAV aerial images into a graph structure, i.e., Maxtree, and detects the power lines under graph signal processing frame. Two-stage filtering is designed to preserve power line components. The preprocessing stage filters out most of the background part according to the color value, and the other stage performs filtering on the Maxtree created with connectivity and gray value. For each node, three attribute components, i.e., gray value, linearity, and length, are assigned to facilitate power line detection. Experiments show that the method can detect power lines accurately and effectively.

Climate variability and vegetation disturbance as critical driving factors significantly affect the regional hydrology in forested watersheds. Yet, due to landscape heterogeneities such as topography, soil characteristics, climate conditions, and vegetation types, hydrological responses to climate and forest changes and associated mechanisms have not been fully understood. The forested Stellako watershed, a typical forest-disturbed area in Canada, has been studied to assess the annual streamflow affected by climate variability and forest disturbance. The study period was from 1951 to 2018. The methods include the modified double mass curve (MDMC), Autoregressive Integrated Moving Average (ARIMA) intervention, and multivariate ARIMA. In the Stellako watershed, 1980 was identified as a breakpoint in the yearly time series between 1951 and 2018. The 1951-1979 period was considered the reference. From 1980 to 2018, the streamflow decreased by 14.69 and 26.18 mm due to climate and forest changes, respectively. The annual runoff variation was mainly attributed to forest disturbances contributing 60.78% of the total variations. Thus, a timely assessment of the runoff variety at a watershed scale has been obtained. The developed methodology can be applied to quantify the disturbance effects at a watershed or regional scale and develop watershed and forest management strategies under future climate and forest changes.

Although the multi-baseline (MB) phase unwrapping (PU) approach does not rely on the phase continuity assumption embedded in the single-baseline (SB) PU approach, it is vulnerable to noise. The two-stage programming approach (TSPA) improves the noise robustness by using the gradient information of the interferogram. However, the TSPA is primarily designed for topography reconstruction. Then, we incorporate the temporal baseline into the TSPA to detect severe deformation, developing the time-series (TS) TSPA (TS-TSPA). The TS-TSPA estimates the absolute phase gradient by combing different temporal baseline lengths and then utilizes the L1-norm optimization model to obtain PU results. Two differential interferograms of 14 April 2021-20 May 2021 and 20 May 2021-1 June 2021 after the Maduo earthquake were showcased. The earthquake had a moment magnitude of Mw 7.4, occurring on 22 May 2021. The quake caused severe surface deformation, deviating or invalidating the phase continuity assumption. With successful PU results by the TS-TSPA, a fault line was identified, and estimated surface displacements ranged from −4.5 to 3.7 m along the line-of-sight (LOS) direction. The PU results of the SB PU method missed the fault line and only detected displacements between 0 and 1.3 m. Therefore, the TS-TSPA effectively detects significant surface deformation where the quake-induced damage could be devastating and outperforms the SB PU method to unwrap phases in damaged areas and fault line delineations.

A new method for monitoring the landslide percusor by soil moisture changes from closure phases is studied. The closure phases are created with an interferometric synthetic aperture radar (InSAR) dataset of triple pairs of interferometric phases. A closure phase of zero suggests no change, whereas a non-zero means change. Then, the decorrelation phases derived from the closure phases were separated by solving the linear programming (LP) convex problem. The decorrelation phase is linked to the soil moisture change. The method's effectiveness is demonstrated with the 2018 Baige landslide event and analysis of multi-temporal Advanced Land Observation Satellite-2 (ALOS-2) datasets. There was a drastic change in soil moisture before sliding. Thus, a new choice is provided for identifying potential landslide precursors by analyzing soil moisture changes.

Distributed scatterers (DSs) should be included in multitemporal interferometric synthetic aperture radar to improve the spatial density and quality of monitoring points. As a key step, phase estimation can significantly reduce the decorrelation of DSs by exploiting all available interferograms. The current phase estimation algorithms are known to be affected by the coherence bias. In this study, we propose an improved DS phase estimation approach, which uses a refined coherence bias correction algorithm. To demonstrate the effectiveness of the proposed approach, we apply it over 50 simulated synthetic aperture radar images. The coherence bias can be significantly reduced by the proposed approach, including an average coherence bias reduction of more than 35% over the existing bias correction algorithm. The reconstructed phase series obtained by the proposed approach have higher accuracy than the current methods.

Multibaseline (MB) interferometric synthetic aperture radar (InSAR) is an advanced variant of conventional InSAR that aims to enhance the accuracy and reliability of phase unwrapping (PU). Among the PU methods employed in MB-InSAR, the maximum likelihood (ML) method offers an optimal solution for phase estimation. However, its limited noise robustness has hindered its practical applicability. To address this limitation, we propose a novel approach, named InSAR phase probability density function (PDF)-to-height/deformation (PDF2HD), which leverages a newly introduced deep convolutional neural network (DCNN) with exceptional anti-noise capabilities. The PDF2HD method employs U-Net and residual network to estimate the InSAR PDF, enabling it to mitigate the influence of phase noise. We present experimental results using two simulated MB InSAR datasets to demonstrate the effectiveness of our proposed method for both digital elevation model (DEM) reconstruction and deformation monitoring.

The phase unwrapping (PU) for Permanent Scatterers (PS) is a key step to obtaining accurate urban mapping results (e.g., a height of a high-rise building) in the time-series InSAR workflow. The phase continuity assumption restricts the existing single-baseline (SB) PU algorithm, so it cannot obtain accurate results in an urban area with high-rise buildings. At the same time, the multi-baseline (MB) method has poor noise robustness for no use of global interferometric phase information. Here, an MB PU algorithm is proposed based on the two-stage programming approach (TSPA) and the general SB PU workflow for the sparse permanent scatterers. Then, we implemented the proposed method into the PSInSAR procedure and estimated building heights in urban areas of southern Chengdu, China. Satisfactory results were obtained for buildings with various heights as assessed by the Google Earth®image and in situ height measurements. Thus, the developed method is valid and effective in building height retrievals in urban areas.

Two-dimensional (2-D) phase unwrapping (PU) is an essential step in interferometric synthetic aperture radar (InSAR) analysis. Although theL0-norm PU method is desired, it is nondeterministic polynomial (NP)-hard. Thus, many PU methods have been proposed to find near-optimal solutions of theL0-norm. As PU is an ill-posed problem, it is difficult to choose the method with the most accurate solution unless reference data are available. In this letter, we prove that the NP-hardness of theL0-norm is weak, suggesting that the α-approximation algorithm of theL0-norm can be devised, i.e., the obtained near-optimal solutions can be within a factor of α of theL0-norm optimal value. The primary contribution of the proof is that the α value of each obtained PU solution can be considered as a new index to assess the PU performance without using reference data. The validity and effectiveness of the α-based index have been verified using simulated and acquired interferometric datasets and three PU methods.

A two-step algorithm to delineate urban targets in PolSAR data was studied. First, we used an eigenvalue- and eigenvector-based decomposition algorithm without the azimuth symmetry assumption to extract three types of scattering components of radar targets. The odd scattering of urban targets was weaker than that of non-urban targets. The cross scattering in urban areas with small azimuth orientation (AO) angles was smaller than that in urban areas with large AO angles. Then, considering the relative importance of the odd and cross scatterings, urban targets were delineated. The algorithm was verified using PALSAR2 L-band data, San Francisco, California (CA), USA, where four types of radar targets, including urban targets with small and large AO angles, water surface, and vegetated areas, were studied. Urban targets of a wide range of AO angles have been effectively identified.

The interferometry synthetic aperture radar (InSAR) technique can generate the digital elevation model (DEM) using the interferometric phase of two SAR observations. In this study, we propose an elevation reconstruction algorithm based on deep learning without phase unwrapping and phase-to-elevation conversion. We also incorporate the intensity information and interferometric phase to obtain a more accurate DEM. Our results show that the studied network can effectively reconstruct the elevation even in rugged terrains. Comparative results show that a better DEM can be reconstructed with the inclusion of the intensity data.

In this paper, the fusion method of the weighted median filter Gram-Schmidt transform transplants to the digital signal processor (DSP). Image fusion technology has always been a key technology in the field of remote sensing image processing, but the algorithm is rarely implemented on mobile devices, so the scope of use has great limitations. The algorithm in the paper blends multispectral images and panchromatic images in the same location. The multispectral image is filtered by using a weighted median filter, and then the processed image and the panchromatic image are fused through the Gram-Schmidt transform. The filtering process reduces noise interference in the image, and the fused image combines the advantages of both images with high resolution and high color information. Due to the portability of DSP chips, the algorithm can be mounted on many mobile devices. Reduce the process of data transfer and make the image processing process more convenient.

Due to the range and azimuth velocity of moving targets, severe defocusing occurs in synthetic aperture radar (SAR) images. The traditional ground moving target imaging algorithm generally needs to estimate the parameters of the moving target, and then conduct the refocusing of the moving target according to the estimated parameters. In this paper, a SAR moving target refocusing algorithm based on generative adversarial network (GAN) is proposed without estimating the motion parameters of the targets. To get a sufficiently trained network, we propose to use simulated moving target data to train the model and evaluate its performance using real data. The results of numerical experiments show that the trained network using simulated data can be well transferred to real test data and effectively achieve to refocus multiple moving targets with distinct velocities at the circumstance of heavy noise.

Airborne synthetic aperture radar images are easily smeared by the phase error due to the unsteady platform movement. Autofocusing by traditional methods is unsatisfied in critical condition of homogenous targets with large degree of defocusing. This paper proposes a one-step end-to-end autofocus method base on Unet with residual blocks (Res-Unet). We use smeared SAR image of a certain area of one scene for model training and test the trained network to autofocus the images of the remaining areas. Numerical experiments are conducted on real airborne SAR data and the results show that the method can achieve well-focused images for target scene with a large degree of defocusing. Comparison results also demonstrate that the proposed improved U-net structure with residual blocks far outperforms the conventional U-net in the task of SAR image autofocusing.

In the small baseline subset (SBAS) algorithm, the zero-value closure phase assumption facilitates two-dimensional single-baseline phase unwrapping (PU) and then derives the surface deformation time series. The premise can fail when significant decorrelation occurs. Thus, we propose an optimization-based PU method by integrating the minimum-cost flow (MCF) model with the minimized closure phases. Three differential interferograms in 2017, i.e., 26 May-07 June, 07 June-19 June, and 19 June-26 May, Mao County of China, were unwrapped with the proposed method. The closure phases ranged from −6 to 7 rad, with an average of 0.01 and one standard deviation of 0.77 rad. Image cells having closure phases within ±1 rad are about 71%. Comparatively, closure phases of the MCF method were ±18 rad, with a mean of 0.55 and one standard deviation of 3.45 rad. Only ~30% of phase values were within ±1 rad. Hence, the studied PU method effectively minimizes the closure phases, thus potentially improving the deformation-related phase unwrapping used in the SBAS algorithm.

In a multi-temporal (MT) interferometric synthetic aperture radar (InSAR) algorithm, if the Itoh condition is not met, the closure phase of zero or near zero is no longer true. To resolve it, we apply a phase gradient network (PGNet) to assess a cell's phase gradient not subjected to the Itoh condition. The PGNet has been trained over rugged terrain in Mao County (32.06°N, 103.65°E), Sichuan Province, China. Then, the network was applied to predict phase gradients of three differential interferograms, i.e., 18 July-30 July, 30 July-11 August, and 11 August-18 July of 2017, Jiuzhaigou, Sichuan. A Ms (surface-wave magnitude) 7.0 earthquake (33.20° N, 103.82° E) occurred on 8 August 2017 in Jiuzhaigou. The PGNet has 1,175,000 cells with 0 for the horizontal closure phase gradient and 1,149,000 for the vertical closure phase gradient. (n = 1,413,120) For the Itoh condition-based method, the cell numbers are 984,800 horizontally and 1,094,000 vertically, less than those after the PGNet. Thus, the PGNet is more effective in minimizing the closure phase than the Itoh condition, improving an MT-InSAR algorithm's performance.

The Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) identifies persistent scatterers (PS) for surface deformation study. The selection of PS is important for obtaining reliable phase information. A novel deep convolutional neural network, namely PSNet, for identifying PS has been studied. The significant advantage of the PSNet lies in its deep architecture to learn characteristics of PS from enormous training images with different topography and landscapes. With the combined feature images composed of the average amplitude, amplitude dispersion, and coherence of interferograms as inputs, the PSNet was trained to classify the PS and non-PS. The results demonstrated that the PSNet delineated PS and non-PS pixels well. The number of PS obtained by the PSNet is more than doubled compared to the number of PS detected by the StaMPS algorithm.

Phase unwrapping is essential in interferometric synthetic aperture radar (InSAR) data processing. Currently, deep learning is widely used in the phase unwrapping process. For instance, the one-step phase unwrapping method is excellent because of its strong noise adaptability. The method treats the unwrapping process as a regression problem, which uses the l1 or l2 loss function to constrain the reconstructed phase to be close to the ground truth of the absolute phase. However, no matter whether the l1 or l2 loss function is used, the result may lack details in texture, and the details cannot be well preserved. This is because the l1 or l2 smoothens the output greatly, and the texture detail loss is not intentionally considered. Due to the noise of the wrapped phase, there is speckle noise in the valley part of the unwrapping result. To solve these problems, we study the generative adversarial network (GAN) with mixed loss functions. The texture details are preserved with the trained GAN, and the speckle noise in the valley is significantly reduced.

Pine wilt disease is one of the most dangerous forest diseases. Because of its strong infectivity and harm, it is very important to find out and stop it in time. In this paper, a novel spatial-spectral random forest (SRF) algorithm for pine wilt monitoring is proposed, for solving the problem of small manual detection range, long investigation time, and untimely discovery of the diseased tree. The proposed method organically combines spatial features with spectral information to quickly and efficiently mark the location of diseased trees. In this way, the online monitoring of the target area using the data of the Beijing-2 satellite is realized. This paper analyses the location of diseased trees and provides early warnings for disease-prone trees. The accuracy of the proposed algorithm is 86.66%, by the confusion matrix analysis.

Trees in flat and wet forest floors and urban targets with small orientation angles can produce strong double scattering. Then, misclassification of trees as urban targets occurs in the PolSAR data decomposition. This study proposed an improved decomposition algorithm for the urban and forest targets classification. First, strong double scattering between forest and urban targets was differentiated, and then forest targets were treated as azimuthally symmetric radar targets in the PolSAR decomposition algorithm. The improved decomposition algorithm was verified by two L-band PolSAR datasets, a UAVSAR dataset northwest New Bern, NC, USA, and one ALOS PALSAR dataset San Francisco, CA, USA. In the datasets, the double scattering was dominant for forested areas. The double scattering dominated some urban areas, but other urban areas had strong volume scattering. Nevertheless, the revised decomposition algorithm correctly delineated both types of targets and extended the usability of the original decomposition algorithm.

A deep convolutional neural network (DCNN) has been widely applied to the 2-D phase unwrapping (PU) in synthetic aperture radar interferometry (InSAR). Our previously-developed PGNet and BCNet outperform the model-based 2-D PU methods. However, the two networks can be further improved. As the PGNet is limited to estimating the phase gradients within ±2π, unwrapped phases can be incorrectly unwrapped sometimes. The BCNet is sensitive to the high-density distribution of the residues caused by a noisy interferogram, resulting in many isolated regions. To solve both issues, we bridge the PGNet and BCNet, studying a new DCNN-based 2-D PU framework (PG-BCNet). The results show that the PG-BCNet is more noise-robust than that of the BCNet and overcomes the limitation of the PGNet that cannot unwrap the phase gradients beyond ±2π.

Deep convolutional neural networks (DCNN)-based methods have been applied widely to ship detection in SAR images. However, most DCNN-based ship target detectors that focus on the detection performance ignore the computation complexity. We propose a lightweight anchor-free ship detection network (LASDNet) for SAR images to tackle this problem. First, a lightweight backbone utilizing a double fusion with squeeze-and-excitation-bottleneck block under the CSPNet design (CSP-DFSEB) and three pooling blocks (i.e., EVE, FCT, and ME blocks) are constructed, which achieves a balance between accuracy and efficiency. Second, a transformer-based aggregation layer conducts feature fusion. Finally, an improved one-stage anchor-free detector FCOS is presented. The analyses of the High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation (HRSID) dataset show that the proposed detector has the second least number of parameters (1.15 MB), the lowest computation complexity (1.01 GFLOPs), and the highest average precision (59.25) compared with other state-of-the-art methods.

The multi-baseline (MB) interferometric synthetic aperture radar (InSAR) technique is not subjected to the Itoh condition, i.e., phase continuity. The technique is particularly suitable to unwrap the wrapped interferometric phases and create a digital elevation model (DEM) in rugged terrain, where the permanent scatterers (PS) are usually scarce. This study shows that the technique satisfactorily unwrapped the wrapped phases with sparsely distributed PS and created a DEM for an area in Himalaya Mountain Range, China. In comparing three DEMs, two created by the single-baseline InSAR approach and one by the MB InSAR technique, the technique outputs the best DEM, as evaluated with a reference DEM.

Airborne synthetic aperture radar (SAR) with an imaging resolution of up to 0.03 m is developed. However, the imaging process suffers from motion errors with 2-D spatial-variant characteristics that invalidate approximations suitable for motion compensation (MOCO) in a submeter resolution SAR system. To estimate and compensate for 2-D spatial-variant motion error (2-D SVME), we propose a novel two-stage processing framework for the ultrahigh-resolution microwave photonic (UHR MWP) airborne SAR imaging. In the first stage, the two-step MOCO compensates for the spatial-invariant and range-variant motion errors. Range downsampling and azimuth windowing are adopted to increase the robustness of the method. Afterward, the coupling of the 2-D SVME is greatly decreased, and a coarse-focused image is obtained. In stage two, an extended autofocusing method in the 2-D wavenumber domain based on the extended range migration algorithm (ERMA) compensates for the azimuth-variant motion errors and nonsystematic range cell migration (NsRCM) for 2-D wide-swath stripmap SAR data. After the ERMA and obtaining the coarse-focused image, the analytical structure of the residual 2-D phase error in the wavenumber domain is revealed. A nonlinear scaling equation is developed, thus relating the 1-D azimuth phase error to the 2-D phase error correction. The Ku-band stripmap UHR MWP (0.03 m) airborne SAR data are analyzed to verify the necessity and effectiveness of the proposed framework. A well-focused stripmap SAR image is obtained.

Multiple azimuth channels (MACs) synthetic aperture radar (SAR) can theoretically achieve high azimuth resolution and wide swath (HRWS). Nevertheless, in practice, channel mismatch will lead to ghost or azimuth ambiguities, which will degrade the imaging quality. This article proposes a novel idea for estimating the channel mismatch of MACs SAR in the image domain. First, we found that the degree of freedom (DOF) of MACs signals doubles after signal reconstruction and imaging. As a result, when the channel number is not great enough, the subspace method for error estimation is unable to be implemented. To deal with this problem, we introduce a DOF compression method based on spectral filtering. This method can decrease the image-domain DOF. Finally, an image-domain subspace method is proposed to estimate the channel phase error, using the focused data and selecting the high SNR region of SAR images. The proposed method has advantages for the channel phase error estimation. Simulated space-borne MACs SAR data and real measured airborne SAR data are processed to demonstrate the effectiveness of the proposed method.

The nonlinear variation of viewing angles over a long duration causes a nonlinear initial phase of the received pulse in a passive positioning system with a single moving receiver. Typical positioning systems ignore the phase and perform incoherent accumulation of the long-time data, resulting in a decrease in positioning accuracy, especially at a low signal-to-noise ratio (SNR). A novel passive positioning system with a synthetic aperture technique, named synthetic aperture positioning (SAP) system, is proposed to resolve the issue. First, a new 2-dimensional (2-D) continuous sampling working model is proposed. Then, the SAP system and a cost function are given to analyze the positioning performance. Third, a positioning algorithm based on the maximum likelihood estimation (MLE) is studied to handle the cost function and position the emitter. Simulation and experimental results verify the validity and effectiveness of the proposed SAP system.

Interferometric phase denoising is a vital procedure for interferometric synthetic aperture radar (InSAR)-based remote sensing techniques because it can improve the accuracy of the final InSAR product. Here, we propose a deep convolutional neural network (DCNN)-based InSAR phase denoising method, abbreviated PDNet. Given an ideal wrapped phase, φ, the PDNet learns the self-similarity function of φ from the input interferogram. After training, the PDNet obtains filtered wrapped phases using the maximum-likelihood approach by exhausting all φs from –π to π. Unlike a boxcar-based filtering method, the PDNet does not consist of an “averaging operation” on the spatial domain, and the resolution loss and interferometric fringe distortion will not directly affect the PDNet result. Thus, the PDNet can be considered a nonlocal phase denoising approach. Analyses and results show that the PDNet is an almost near-real-time denoising algorithm. Its denoising accuracy is higher than that of the available model- and learning-based InSAR phase denoising methods.

A technique able to acquire the height, especially for an area with a large spatial extent, is of great interest for forest biomass estimation. In this study, an inversion algorithm for tree height using the InSAR technique and dual-pol ALOS/PALSAR datasets was studied. The algorithm used the interferometric phase difference, coupled with two height compensation factors. When applied in the tree height estimate at Duke Forest, North Carolina, USA, it performed satisfactorily. As verified by in situ height measurement, the algorithm should have the potential to be one possible approach to estimate tree heights at regional and global scales.

A polarimetric interferometric synthetic aperture radar (PolInSAR) approach can estimate tree heights, but underestimation happens. There is a need to mitigate the underestimating. With the Freeman-Durden polarimetric SAR (PolSAR) decomposition algorithm, one identifies the volume, single, and double scattering components from PolSAR data. Here, the PolSAR decomposition and PolInSAR techniques are combined to estimate a tree height. The decomposed volume scattering phase center replaces the canopy phase center and the double scattering phase center the ground phase center. The tree height is then calculated using the phase difference of the two phase centers and effective vertical wavenumber. Using simulated PolSAR datasets, the algorithm combining the decomposition and PolInSAR approaches output tree heights with an average of 8.6 m and one standard deviation of 2.1 m. The mean and one standard deviation of tree heights are 5.1 m and 2.4 m after the PolInSAR technique. Thus, an improvement of 3.5 m in the mean height is achieved.

Urban targets with large azimuth orientation angles and vegetation targets have strong volume scattering in polarimetric synthetic aperture radar (PolSAR) data. Their separation is a challenge. The real parts of ShhShv* and SvvSvh* correlate to the azimuthal symmetry of radar targets in the PolSAR data. The values are close to zero for azimuthally symmetric vegetation targets and much away from zero for azimuthally asymmetric urban targets. Also, in the urban area with large azimuth orientation angles, the real part values of ShhShv* are negative, but the real part values of SvvSvh* positive. Thus, the sum of the real parts of ShhShv* and -SvvSvh* are studied as a new separation descriptor, Sd, to delineate the urban targets from vegetation targets. The descriptor is evaluated by using the 2009 PALSAR San Francisco PolSAR data. The Sdvalues are negative in the urban area where targets with large azimuth orientation angles exist but close to zero in the vegetated area. Correct separation rates between the urban and vegetation targets are 92.44%-99.5%, with an empirical threshold. Thus, the descriptor can effectively differentiate urban targets with large azimuth orientation angles from vegetation targets.

An algorithm using slow feature analysis and a cloud-free auxiliary image was proposed to remove thin clouds within Landsat-5 visible and near-infrared (VINIR) band data. The study area was near Norwalk, CA, USA. Five$800\ (\text{rows})\times 800$(columns) Landsat-5 images acquired on different dates were studied. Image one was cloud-covered. Image two, a cloud-free one acquired 16 days after image one, was used as the reference image. Images 3–5 were three cloud-free auxiliary images. The algorithm without using an auxiliary image was applied to removing thin clouds in image one. Some clouds were removed, but cloud residuals remained. With the reference image,$R^{2}$values were computed, ranging from 0.6039 to 0.8750 for four bands. Then, the algorithm using auxiliary image one was employed to remove the clouds in image one. The removal was improved as the residuals decreased and$R^{2}$values increased. The algorithm's performance might not be so sensitive to auxiliary images' acquisition dates as the cloud-removal results using images 3–5 were similar.

Phase unwrapping is a key step of interferometric synthetic aperture radar (InSAR), which transforms wrapped phase into the absolute phase. The accurate phase unwrapping requires high signal-to-noise ratio (SNR) value. Thus phase filtering is necessary to filter out the noise. This paper proposes an improved U-Net neural network for phase filtering. The images of noisy and noiseless interferometric phase are fed into the network for training. With the trained neural network, the noisy input interferometric phase can be directly smoothed to obtain high SNR phase image. Experiments show that the network can filter out phase noise effectively in the phase contiguous region, meanwhile it keeps the structure information of phase ambiguous edge region.

The fusion of hyperspectral image (HSI) and light detection and ranging (LiDAR) data can provide complementary information and improve the accuracy of land cover classification. In this paper, a novel fusion method is proposed to fuse the HSI and LiDAR dataset based on multi-scale feature extraction and total variation. In the method, the extended multi-attribute profile (EMAP) is utilized to automatically extract structural information from HSI and LiDAR elements. The extracted features are then estimated in a lower-dimensional space by multi-scale total variation (MSTV). Finally, the classification map is generated by applying random forest classifiers on the fused data. In the experiment, the performance of the proposed method is evaluated on an urban dataset of Houston. The results demonstrate that classification accuracy could be significantly improved by the proposed method compared with other methods.

In the SBAS-InSAR process, the conventional approach of determining ground control points (GCPs) can be error-prone. Thus, a new GCP selection approach using the phase derivative variance is proposed. Analyzing 45 descending-orbit Sentinel-1 datasets between October 2014 and June 2017 over the 2017 Xinmo landslide site, we separately identified ~10,600 and 100 GCP candidates with the conventional and proposed approaches. With four GCPs from the conventional method, the time-series displacement curves do not capture the accelerated deformation related to the landslide event. Corresponding to two sets of GCPs from the proposed approach, the displacement curves change gradually and capture the accelerated deformation. Thus, the approach can efficiently identify a small number of reliable and accurate GCP candidates for the SBAS-InSAR analysis.

The spatiotemporal fractional vegetation cover (FVC) variations from 2000 to 2020 in the Changjiang basin, China, were studied. With Google Earth Engine (GEE), thousands of Landsat-5, 7, and 8 images were analyzed. In 2000, 2010, and 2020, the FVC increased roughly from the west to the east, crossing the basin. The low FVC areas were mainly around the Qinghai-Tibet Plateau and Changjiang Delta. The basin was well-vegetated from 2000 to 2020, with a minimum yearly average FVC of 67.45%. The FVC increased. At the pixel level, 60.14% of locations had a positive slope of the FVC versus time. Thus, the ecological environments assessed by FVC were healthy for the last 20 years, and the health improved. As shown in this study, GEE is an efficient and effective platform to study natural environments and environmental changes at a large spatial extent and over a long time.

Phase unwrapping (PU) technology plays a decisive role in interferometric synthetic aperture radar (InSAR) workflow. Due to the limitation of InSAR system, the interferometric phase can only be measured between (-π, π), which called wrapped phase. In order to overcome this limitation and obtain the absolute phase, phase unwrapping algorithm came into being. The least square phase unwrapping algorithm is one of the most popular phase unwrapping methods, which can be solved by fast Fourier transform (FFT) and has high efficiency. It means that this algorithm can waste less time to obtain the InSAR products, while its accuracy is often unstable. This paper is dedicated to overcoming this instability through combining deep learning with fast least square method. The specific steps include: 1. Use deep learning to predict the phase gradient stably regardless of the phase quality; 2. Replace the wrapped phase gradient of least square unwrapping algorithm by above prediction result and take the least square solution. The simulation and real data experimental results demonstrate the effectiveness of this improved method.

With a moving SAR platform, the HV (H transmitted and V received) and VH (V transmitted and H received) components in the polarimetric synthetic aperture radar (PolSAR) data are obtained at different positions along the azimuth direction. The HV and VH datasets' correlation coefficient varies whether a ground radar target is stationary or not. A building is standing, but a tree canopy may not be due to the surrounding air movement. Therefore, based on the correlation between the HV and VH components in one single PolSAR dataset, a stationary descriptor,$S_{D}$, was studied to separate the volume scattering from a stationary urban target with a large azimuth orientation angle or a non-stationary vegetation canopy. The separability was evaluated using two PALSAR PolSAR datasets. The correct separation rates were 86.2% for urban targets with large azimuthal orientation angles and 93.9% or higher for vegetation targets. Thus,$S_{D}$effectively differentiates urban targets with large azimuth orientation angles from vegetation targets.

Convolutional neural networks (CNN) can automatically learn features from the hyperspectral image data, which could avoid the difficulty of manually extracting features. However, the number of training set for the classification of hyperspectral images is always limited, making it difficult for CNN to obtain effective features and resulting in low classification accuracy. In this paper, a spectral-spatial feature (SSF) extraction based CNN method is proposed for an accurate classification with a small training set. Experimental results based on two standard hyperspectral images demonstrate the effectiveness of the proposed method.

Modeled thin-cloud components using the RTM-based algorithm may include the reflectance of ground targets. If the components are then used in cloud removal, the ground reflectance can be adversely affected. Thus, we revised the modeled components and developed a thin-cloud removal algorithm. With Landsat-8 data acquired on 29 March 2014, the spatial correlation coefficient between Landsat-8 Band-9 and revised cloud components was the highest. The revision of the modeled components was quantitatively satisfactory. The spatial correlation coefficients for Bands 1-4 between the cloud-removed and cloud-free reference images ranged from 0.687 to 0.797. In addition, the algorithm had the smallest impact on the cloud-free pixels. Overall, this algorithm outperformed the original RTM-based algorithm. Therefore, we have achieved the objective to remove thin clouds but to preserve ground features.

Filtering is one important step in the post-processing of the LiDAR point-cloud data. The progressive triangulated irregular network (TIN) densification (PTD) filtering is widely recognized. However, the PTD sometimes filters the ground as non-ground points in rugged terrain and it is sensitive to threshold parameters that are manually set. To mitigate both shortcomings, we developed a new algorithm using the techniques of the segmentation and terrain-adaptive threshold parameters. A benchmark dataset provided by ISPRS was employed to compare the performance of our and three widely-recognized LiDAR filtering algorithms. The total error (5.54%) and Type I error (4.37%) produced by our algorithm was the smallest in separating the ground and non-ground points. The Type II error was 15.50%. The DEM derived from the filtered ground points consisted of characteristics for rugged terrain. Thus, the developed algorithm was valid and effective.

Phase unwrapping (PU) transforms wrapped phase into the real one, so that the products of interferometric synthetic aperture radar (InSAR) can provide meaningful information, such as surface height and deformation. By analyzing the characteristics of phase integer modulus, we propose a new phase unwrapping algorithm which combines deep learning and region segmentation with ambiguity number. Because this algorithm calculates the ambiguity number directly, it can improve the phase preserving property of the unwrapped phase and greatly reduce the computation complexity. The simulation and real data experimental results demonstrate the effectiveness of our methods.

The tree height estimation using the simplified three-stage algorithm and HH and HV polarizations or dual-polarization data was studied. First, we considered (HH+2×HV) as VV data obtaining the simulated quad-polarization data. Then, the polarization interference coefficients were computed. To reduce the combined effort of the HH and HV polarizations on the interference coefficients, we revised the coefficients. Both sets of coefficients were used in the estimation of tree heights. The simulated mean tree height was 18 m. The estimation was improved when the revised coefficients were used. The mode of heights changed from 20.0 m to 18.4 m before and after the revision. Thus, the estimated tree heights should be acceptable. The required input of the quad-pol data to the three-stage algorithm was simplified with the input of the dual-pol data.

A revised RTM-based algorithm was developed coupled with the classification of land cover types. The study area was near Cedar Island National Wildlife Reserve, North Carolina, USA. A cloud-covered Sentinel-2A image of 1000 (rows) × 1000 (columns) acquired on 2 February 2017 was studied. The area was classified into three land cover types of vegetation, bare soil, and water. The assumption that the linear relationship of the TOA reflectance of any two visible bands was independent of the terrain types in the RTM-based algorithm was not valid. Thus, the impact of the land cover types on the algorithm was analyzed. The cloud-removal algorithm should be separately developed based on each land cover type removing the clouds in each land cover type accordingly. This assessment was evidenced by the spatial correlation coefficients of the cloud-covered image and the cloud-free reference image, of the cloud-removed image using the revised RTM-based algorithm and the reference image, and of the cloud-removed image using the original RTM-based algorithm and the reference image. The revised algorithm was not only effective in the removal of thin clouds land cover type-by-cover type but also outperformed the original RTM-based algorithm.

We propose a deep learning method for non-cross term and high-resolution time-frequency analysis. There is a tradeoff between cross term suppression and time-frequency resolution in time-frequency analysis (TFA). By exploiting the end-to-end learning property of convolutional neural network (CNN), we propose a new TFA method, which uses low-resolution short-time Fourier transform (STFT) as input, and outputs high-resolution time-frequency distribution (TFD). By stacking high-resolution instantaneous Doppler lines from different range cells, we will obtain high-resolution inverse synthetic aperture radar (ISAR) image of maneuvering target. Simulation result demonstrates the effectiveness of the proposed method.

High-resolution radar imaging will give us detailed information of target, which becomes basic function of radar systems. Improvement of image resolution of the existing radar system is also important. Based on deep learning, a new method for super-resolution through-the-radar imaging is proposed. A network, called cascade U-net (CU-net), is proposed in this paper. The results of simulation and real data experiments demonstrate the effectiveness of our methods.

Producing a landslide susceptibility (LS) map using the statistical techniques such as the density ratio heavily relies on an existing inventory dataset. In the absence of the data in Western Area, Sierra Leone (Africa), the SBAS-InSAR technique was applied for detecting the ground deformation using multi-temporal Sentinel-1 SAR datasets from July 2015 to August 2017. The derived slope displacements coupled with the geomorphological evidence in the ancillary data are used to map the possible landslides in the area. The density ratio technique was used to generate landslide parameter class values. The values are aggregated to create the LS map and the result validated using the degree of fit and the error index. This paper, therefore, highlights a method of creating the landslide inventory and susceptibility in areas where the inventory data are limited or even absent.

Monitoring ground surface deformation is one key for an early warning system to mitigate against landslide damages. In this study, the SBAS-InSAR technique was applied to analyze forty-seven SLC Sentinel-1A C-band SAR datasets acquired in the interferometric wide-swath (IW) mode for the detection of the time-series surface deformation leading to the Regent landslide events in 2017, Sierra Leone. The potential landslide sites were also identified in the area. It is argued that if the monitoring program using the SBAS-InSAR technique were in place in 2017, an early warning of the imminent slope failure could have been given some days before the landslide events occurred on 14 August 2017.

In this study, integrating Landsat-derived land uses in continuous years with the Soil and Water Assessment Tool (SWAT) hydrologic model, we have quantified the inter-annual attribution of runoff change in the Qingliu River, China. We found that runoff increased insignificantly during 1960-2012 and changed abruptly in 1984. The land use changed year-by-year mainly from forest and farmland to residential area. Climate variability dominated runoff increase (with a mean of 115.77%) during 1989-2012 except for 2005 and 2007, during which human activity was the main contributor. In comparison, land use changes increased runoff and exhibited relatively small contribution to the runoff change over time. The findings would help people better understand the behavior of the Qingliu River and benefit decision-makers for adaptive water resources management in a changing environment.

A revised independent component analysis (ICA) algorithm was developed to improve the performance in the removal of cirrus cloud effects within spectral data of Landsat-8 Bands 1-7. The prior was that the reflectance values in the cirrus spectral band should be zero or near zero after the cloud removal. Then, the cloud component derived using the forward ICA transformation was revised and use to remove the cloud effects. After the inverse ICA transformation, the cloud removal was completed. Applying the algorithm to the Landsat-8 sub-image of 041/036 (path/row) acquired on 14 March 2015, we noted that cirrus clouds in Bands 1-7 disappeared visually. The cloud-free sub-image of the same location acquired on 1 April was extracted and used as the reference image in the algorithm verification. After the algorithm, R2values of the March image and reference image increased significantly. The revised algorithm was effective and valid. The revised one outperformed the original ICA algorithm in the comparison of both algorithms.

After revaluating the validity of the assumption made in the existing decomposition algorithms for the quad-pol SAR datasets, we proposed a two-step algorithm to resolve the invalid assumption. The algorithm was assessed using the quad-pol ALOS/PALSAR and NASA/JPL UAVSAR datasets. The results were satisfactory. The algorithm should be valid.

Under the interferometric wide swath (IW) mode, the Sentinel-1 synthetic aperture radar (SAR) incidence angles vary from 29.0° at the near range to 46.0° at the far range. When the side-looking SAR datasets are used in the rugged terrain, the observable areas are functions of the SAR incidence angle and the local slopes and aspects. Thus, the application of the data is influenced. In this study, the 2017 Xinmo landslide event is used to demonstrate the influence. Before the landslide event, the observable areas at the initial landslide zone varied from 200,000 m2at the incidence angle of 29.0° to 80,000 m2at the incidence angle of 46.0°. The reduction of the observable area should be significant. In the extreme case, the visible area might become too small such that the InSAR-derived result could not be reliable. Caution should be exercised. Finally, knowing the observable area should complement the InSAR analyses of the coherence, interferograms, surface deformation rate, and deformation through time.

The soil moisture (SM) of Zoige Wetland, China in 2017 were analyzed using the multi-temporal Sentinel-1 dual polarization ground range detected datasets. The VV-derived SM data were suitable for assessing the SM in Zoige Wetland. In 2017, the SM at different land cover types had similar change patterns. The SM increased from January to August and then decreased until the end of the year. The patterns were expected with the intra-annual variations of temperature and precipitation. This study demonstrated the ability to retrieve SM at Zoige Wetland using the multi-temporal Sentinel-1 datasets.

The cirrus band (Band-9) of Landsat-8 Operational Land Imager (OLI) can well detect cirrus clouds, which should be the desired supplement to the existing thin-cloud removal algorithms. Thus, the cirrus band has been incorporated into an available RTM-based algorithm to remove thin clouds in the visible bands of Landsat-8 data. The reason to add Band-9 to the RTM-based algorithm was to suppress the ground information contained in the modeled thin-cloud reflectance data of the visible bands. The new algorithm was effective and valid in the thin-cloud removal as shown in this study. The algorithm outperformed the original RTM-based algorithm as well.

The surface deformation of the eastern section of the third-ring bypass as well as its surrounding areas, Chengdu, China was studied. The Permanent Scatterer (PS) interferometric synthetic aperture radar (InSAR) technique and Sentinel-1 multi-temporal datasets acquired along the ascending and descending orbits were used. With sixty-two ascending orbit datasets (from July of 2015 to May of 2018) and sixty-nine descending orbit datasets (between October of 2014 and May of 2018), the subsidence occurred in the study area mainly. The overall spatial patterns of the deformation derived from the ascending and descending SAR observations were similar. Five locations with significant subsidence were identified and analyzed. The causes for the subsidence were attributed to the construction and the operation of the subway system and highway overpasses, and the local commercial activities.

Clouds are a common barrier of satellite optical images and adversely affect applications of remotely sensed optical data sets. The optical thickness of clouds varies spatiotemporally. The thickness can be very thin making the detection of thin clouds difficult. A new cirrus band (Band-9) of Landsat-8 has been added to detect thin clouds. However, the majority of spaceborne optical sensors existed previously or in operation do not have the cirrus band. An algorithm is developed to detect thin clouds without using a cirrus band. In particular, the top-of-atmosphere reflectance of thin clouds is modeled using the empirical relationships of the deep blue and blue bands of Landsat-8 Operational Land Imager. A Landsat-8 image of path 14/row 36 near southeastern North Carolina, USA, is used to validate the algorithm. Thin clouds are well-identified when compared to Landsat-8 Band-9 data. The spatial correlation coefficient for both is 93.49%. Therefore, the algorithm is valid. The algorithm is further verified when a blue band and a green band are used to develop the algorithm. Thus, the analytical approach should be extendible to Landsats 4, 5, and 7 sensors or optical sensors as long as they have a blue band and a green band. Finally, the applicability of the algorithm under various atmospheric conditions is verified after analyzing two water vapor absorption spectral bands of NASA/JPL Airborne Visible/Infrared Imaging Spectrometer data.

Kangding Airport, Sichuan, China located on the eastern margin of the Qinghai-Tibet Plateau has been developed in rugged terrain. The airport environment is characterized by high filling of earth materials, high frequencies and intensities of seismic activities, and high altitude. The understanding of potential risk of the 3-high airport assessed by the surface deformation is of vital importance. In this study, the Small Baseline Subset (SBAS) and Quasi Persistent Scatterer (QPS) techniques were used and proven to be effective to quantify the long-term surface deformation of the airport. The deformation was highly correlated with the local geological settings and annual climate cycle.

An algorithm to remove the impact of man-made targets on tree height estimation using the three-stage algorithm and PolInSAR data was studied. The data were the German/DLR E-SAR L-band PolInSAR data near Oberpfaffenhofen, Germany. Within the data, there were forested areas and man-made targets such as runways and buildings with variable azimuth angles. The runways with smooth surface or buildings with large azimuth angles were sequentially removed by the mask of surface and non-surface scattering components derived from the PolSAR decomposition algorithm, and the mask of high and low coherence coefficient. Then, tree heights were estimated. Results were satisfactory. Therefore, the developed algorithm was valid, and the applicability of the three-stage algorithm has been extended.

The RTM-based algorithm developed to remove thin-cloud effects for Landsat-8 visible bands was extended to those of Sentine-2A visible bands. In the assessment of the extendibility, a cloud-covered Sentinel-2A image acquired on 18 February 2017 was downloaded. Another cloud-free Sentinel-2A image of the same area acquired on 8 February 2017 was selected as reference image. The three assumptions made in the original RTM-based algorithm were evaluated, and were valid for Sentinel-2A data. The algorithm was qualitatively and quantitatively effective in the thin-cloud removal for Sentinel-2A data of visible bands. Therefore, the algorithm developed for Landsat sensors was directly applicable to Sentinel-2 spectral data.

Coupled with the independent component analysis (ICA) and estimated thin-cloud reflectance data, an algorithm to remove thin-cloud effects on the operational land imager (OLI) data of Landsat-8 was studied. The algorithm was evaluated with a subimage extracted from Landsat-8 041/036 scene acquired on 16 March 2015. Thin clouds were effectively removed visually after the algorithm. In the algorithm validation, a cloud-free Landsat-8 image acquired on 1 April 2015 was selected as the “truth”. Spatial correlation coefficients of the March image before the cloud-removal algorithm and April image and of the March image after the algorithm and April image were evaluated on the band-by-band basis. The coefficients increased significantly after the algorithm. Thus, the developed algorithm was quantitatively effective and valid.

Coupled with Landsat-8 and SMAP brightness temperature datasets and using the HP model, an algorithm to estimate soil moisture in November of 2015 at Zoige alpine wetland, China was developed. The algorithm was verified using soil moisture data downloaded at NASA Earthdata web site. The spatial patterns of two datasets were similarly to each other with the maximum value occurring near the center but the minimum value in the eastern region. The spatial correlation coefficient of both datasets was 0.6832. The preliminary findings should be very encouraging in the pursuing to produce soil moisture data with high spatial resolution.

An automatic multi-temporal method is proposed in this paper for cloud detection without known the reference image in prior. A series of reference images are provided by fitting robustly of the pixels of multi-temporal images contaminated by clouds to show the inherent gradual change of the landscape with time instants. Then the cloud is detected by thresholding the difference between the target and the reference images, which is found to be merely composed of the regression model error modeled as Gaussian noise and outliers corresponding to cloud and its shadow. The proposed method is compared with state-of-the-art algorithms on the LANDSAT dataset, and shows a better discrimination of cloud and cloud shadow covered pixels from the uncontaminated ones.

Polarimetric NASA/JPL UAVSAR imagery for the curved and graded Coronado Bridge over San Diego Bay, California, USA was analyzed. The bridge was shown with three types of features. Feature 1 consisted of nearly even distributed dots, feature 2 dots and a curved segment, and feature 3 a continuous curved segment. On the basis of the SAR image geometry and polarimetric decomposition method, dots of feature 1 were produced by the double-bounced interactions of the bridge surface and light poles on the far side of the bridge toward the SAR. Dots of feature 2 were from the double-bounced interactions of the ocean surface and light poles on the near side toward the SAR. The curved segment of feature 2 came from the double-bounced interactions of the ocean surface and side of the bridge facing the SAR. Curved segment of Feature 3 was the multiple interactions of the ocean surface and bottom of the bridge.

The surface deformation of Dujiangyan, Sichuan, China was studied. Permanent Scatterer (PS) interferometric synthetic aperture radar (InSAR) technique was applied to 21 Envisat SAR datasets acquired from May of 2008 to July of 2010 and 61 Sentinel-1 SAR datasets acquired between October of 2014 and November of 2017. The Zipingpu Dam, located on the northwestern highland area of Dujiangyan had obvious subsidence in a 2-year period after the 2008 Wenchuan earthquake. The rate ranged from 0 to −30mm/year. The quake was attributed to the major cause. From 2014 to 2017, the dam was deformed less than 5mm/year. The dam was relatively stable. Since the recovering efforts were completed before 2014, the surface deformation in the urban areas of Dujiangyan was small between 2014 and 2017. The dominant rate was about 2 to −4mm/year.

Xinmo landslide event, China occurred on 24 June 2017. The head of the landslide area was ~3,600m above the mean sea level. Then, along the high to low elevation direction, a stable zone (S1) above the head area, an unstable zone (that is divided into an upper unstable subsidence area, UU; and a lower unstable uplift area, LU), and a stable area (S2) before the landslide event were identified. The time-series surface deformation was studied using the SBAS-InSAR technique and Sentinel-1 multi-temporal datasets acquired between November of 2015 and June of 2017. The mean deformation value was -8.3mm in S1, -26.9mm in UU, 27.9mm in LU, and - 1.6mm in S2, respectively. Zoning characteristics of moving earth materials in a typical landslide event were observed. After removing the deformation values in two stable zones, the difference in absolute value of the subsidence and uplift in the unstable zone was about 80mm, 100mm, and 130mm on 24 February 2017, 19 May 2017, and 24 June 2017, respectively. No landslide event occurred in February and May. Thus, with the preliminary findings, a warning might be issued once the absolute value was greater than 100mm.

Soil moisture of Zoige wetland, China from 2007 to 2009 was estimated using PALSAR radiometrically terrain-corrected (RTC) and Landsat-5 surface reflectance data coupled with water cloud model (WCM) radar backscattering model. Soil moisture derived from L-HH polarization RTC data was suitable for assessing the soil moisture in Zoige wetland. In 2007, the soil moisture in the growing season had similar spatiotemporal patterns and trends, and the intra-annual difference of soil moisture was very small. However, the inter-annual soil moisture increased from 2007 to 2009 substantially. Variations of temperature and precipitation were attributed to the causes.

Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique was used to analyze the surface deformation from 2014 to 2017 at Heifangtai, Yongjing County, Gansu Province, China. The significant subsidence areas were delineated. Five potential landslide sites near the edge of Heifangtai were identified. In comparison of the surface deformation patterns among five sites, site A had the largest deformation value. The deformation might be accelerated after June of 2017. On 1 October 2017, a landslide event happened at site A. Thus, factors closely linked to the event was the magnitude and rate of surface deformation. With the findings coupled with other investigations, one should be able to identify potential landslides using the SBAS-InSAR technique and multi-temporal SAR datasets.

Due to the very high orbit, the apparent features of geosynchronous synthetic aperture radar (GEOSAR) are the curved trajectory and long integration time, which can lead to severe coupling between the azimuth and the range directions and, therefore, complicates the resolution evaluation. The traditional analytical approach based on the 2-D division may produce large resolution error, and the numerical approach may suffer from huge computation burden. Therefore, an analytical resolution evaluation approach for GEOSAR based on the local feature of the ambiguity function is studied in this paper. The proposed approach is validated with simulation data to be of high efficiency and accuracy. In addition, the proposed approach is also demonstrated to be capable of evaluating the resolution for other complex platforms, and of evaluating the 3-D resolution of a SAR system.

Four vegetation indices for four types of dominant vegetation species in Chengdu, China are derived using data collected by a HySpex VNIR-1024 hyperspectral camera. The species are cedar, eucalyptus, locust, and willow. Then, the air quality index (AQI) measured at a local monitoring station, Shilidian is linked to the vegetation indices per species. The 1stand 2ndorder of polynomial models of AQI vs. each vegetation index and for each species are next developed. Variable R2values and root mean squared errors (RMSEs) are evaluated for all 32 models. The best regression model determined by the highest R2value (0.8657), and the smallest RMSE (17.5) is the 2ndorder model of AQI vs. difference vegetation index (DVI) for willow.

A new four component decomposition algorithm for urban targets delineation from PolSAR imagery was studied. First, a new correlation coefficient was introduced to describe characteristics of urban targets. The coefficient was the linear combination of two elements of a coherent matrix. Then, the coefficient was used to modify the volumetric scattering model component in Yamaguchi et al.'s four-component decomposition algorithm with the azimuth deorientation. Thus, a new four-component decomposition algorithm was established. With the algorithm, the percentage of double-bounced scattering increased but the percentage of volumetric scattering decreased in urban areas. Thus, the urban targets whose azimuth angles are 0° or non-zero° were effectively identified.

The influence of the azimuth angle and water surface roughness determined by wind speeds on the SAR imaging of a metallic bridge over water was simulated after the identification of the double- and triple-bounced radar returns. Different bounced returns were caused by interactions of the bridge surface and water surface. In the simulation, the radar wavelength was L-band. The azimuth angle was between 0 and 45°. The wind speed was from 0 to 40m/s. The Pierson-Moskowitz spectra were used to model the spatial spectral energy distribution of the sea surface. The Kirchhoff approximation algorithm was used to compute the backscatter. With the noise level of a SAR system at -30dB, three regions were delineated. In region I, both double- and triple-bounced returns existed. There were only double-bounced returns in region II. None of the double- and triple-bounced returns was in region III.

A method of decreasing the uncertainty in validating moderate resolution remotely sensed Land surface temperature (LST) was established on a heterogeneity surface in Northwest China. Two Landsat-8 images and in situ measured data were used in the paper. The LST and multiband reflectance as well as the reflectance coefficient of variation have a significant regression relation. For two Landsat-8 images, the determination coefficients were both above 0.88, and root mean square errors (RMSEs) were 1.2 K and 1.5 K respectively. Meanwhile, the regression relation isn't sensitive to the change of spatial resolution. High resolution LST which was obtained by using the regression relation and in situ measured LST were used in validating the Moderate Resolution Imaging Spectroradiometer (MODIS) daily LST product MOD11A1. The final result shown that the method can effectively decrease the validation uncertainty.

With the assumption that spectral characteristics of coastal band (Band 1) and blue band (Band 2) of Landsat-8 were similar in a cloud-free area, an algorithm to detect thin clouds was derived. The assumption and algorithm was evaluated using Landsat-8 data of p14/r36 acquired on 1 April 2014. First, the assumed spectral similarity of Bands 1 and 2 was validated in the cloud-free area. The similarity was adversely affected once there was cloud (over the area). Second, the algorithm was applied to detect thin clouds in a full scene of Landsat-8 image. Visual comparison with the true color composite, the detected and observed clouds were very similar spatially. Next, the spatial correlation coefficient of the detected could image and the top-of-atmosphere (TOA) reflectance data of Band 9 of Landsat-8 was computed. The coefficient was 0.9604. Finally, with the least-squared-fit analysis, R2value of 0.9223 was obtained. Since the interrupt of the least-squared-fit equation was -0.0003 or close to 0, there was the simple ratio relationship of the detected cloud and reflectance value of Band 9. Thus, the algorithm was not only able to detect thin clouds, but also to assess them quantitatively.

Monthly methane concentration (CH4) level over Zoige alpine wetland, China from October of 2002 to December of 2006 was analyzed based on the SCIAMACHY data. The level was low and might decrease from January to March then increase until August (except for 2006), and then decreased until fall or early winter. The linkage of the CH4, air temperature, precipitation, and soil temperature and moisture content was then explored by using meteorological and GLDAS datasets. The CH4emission and concentration level in the vertical column over the wetland could be affected more by the temperature than the precipitation.

High resolution synthetic aperture imaging using rotating fan-beam scatterometers is studied. First, the working mode is presented. Then, the relationships among pulse repetition frequency, the number of coherent pulses and the unambiguous swath are discussed. Considering the sparsity of the imaging region and the limited number of pulses, we adopt the sparse recovery method to obtain the target images. By utilizing multiple frequency systems, the unambiguous imaging swath is enlarged. Finally, the simulated results confirm the proposed method.

An approach to estimate tree height of coniferous forest in mountainous areas at plateau was developed using a combined function. One pair of dual Pol- InSAR images acquired on 20 June 2007 and 5 August 2007 by Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-PALSAR) sensor over Zoige plateau, northwestern Sichuan, China was used to evaluate the approach. The derived heights indicated that the combined function approach well compensated for underestimated canopy depth using the phase center methods. Thus, tree heights were better approximated in comparison with actually measured tree heights.

As one of most valuable and vulnerable resources in coastal aquatic ecosystems, submerged aquatic vegetation (SAV) and its state have received great attention. There is an urgent need for coastal managers and researches to monitor and assess spatial and temporal distributions of the SAV rapidly. Because of the repetitive coverage, satellite hi-resolution images are proved to be efficient but not suitable in large-scale spatial and multiple temporal assessment due to the concern of cost. Thus, the objective of this research is to probe into the suitability of Landsat-8 OLI data in the mapping of SAV habitats at Albemarle Sound, North Carolina, USA. With SONAR data as in situ measurement, the overall accuracy based on the number (n) of SAV points detected the SONAR per cell was 66.7% if n ≥ 1, 68.3% when n ≥ 6, and 67.8% if n ≥ 10. The cell size was 15m×15m. Thus, the distribution of SAV beds was efficiently depicted using multi-temporal Landsat-8 data. The phenological changes of SAV were revealed.

An algorithm to simulate the reflectance of clouds using visible bands was developed based on the simplified radiative transfer equation and two assumptions. The assumptions and algorithm were evaluated using a Landsat 8 sub-image of 015/035 (path/row) acquired on 27 August 2013. The relationship of visible bands in clear regions was verified to be linear. The effects of clouds in visible bands were linear as well. Then, the algorithm was used to simulate thin clouds. Band-by-band, the simulated clouds were in agreement with the distribution observed in each visible band. Finally, spatial correlation coefficients of simulated clouds and clouds detected by band 9 of Landsat-8 were calculated. High spatial coefficients quantitatively indicated the validity of the algorithm.

Cloud detection in multi-temporal optical remote sensing images is a significant task. In this paper, we proposed an efficient method to coarsely detect the cloud via Mean Shift Cloud Detection (MSCD) algorithm, which can work automatically without any reference images. Experimental results on Landsat-8 OLI dataset show the effectiveness of the proposed method.

A Moon-based synthetic aperture radar (SAR) system can provide large-scale, long-term, and constant earth observation (EO). Nevertheless, several problems should be solved before implementation. The problems include the ultra-small range of antenna viewing angles, the largest cell size of SAR allowed without the consideration of range cell migration and the related antenna size, and the decorrelation caused by long integration time. Although the moon-based EO system is a concept at present, with a baby but concrete and persistent step, the giant leap for human beings will be achieved.

A novel algorithm to estimate moving target velocity for a spaceborne high resolution and wide swath (HRWS) synthetic aperture radar (SAR)/ground moving target indication (GMTI) system is presented. To retain the power of the moving target, one needs to know the velocity of the moving target before clutter suppression and spectrum reconstruction. According to the relationship between the velocity and cone angles, the estimation of velocity is transformed into the estimation of direction-of-arrival (DOA) of the received signal using the sparse DOA technique. If the clutter is ignorable, the algorithm is directly applied to the received signal. Otherwise a preprocessing based block matrix is performed. Simulated results confirm the effectiveness of the proposed algorithm.

The ultralong synthetic aperture time and a very large scene cause severe 2-D spatial variation in geosynchronous synthetic aperture radar. The range variation was corrected using the range cell migration equalization and the modified chirp scaling function. The azimuth variation correction with the singular value decomposition and the azimuth nonlinear scaling was studied. The validity of the proposed imaging algorithm has been assessed. Satisfactory results were obtained in the removal of the azimuth variation, and the focusing of point targets from a synthetic aperture up to 1000 sand a scene of 150 km (azimuth) × 130 km (range).

Using the polarization as a scale, we compensated for the azimuth scale effect on the building identification in urban areas. With the increase of polarization dimension (from a single polarization, dual-polarization, to polarimetric data with the rotation of azimuth angle) or change of polarization scale, the azimuth scale effect was gradually resolved.

An algorithm to remove thin clouds within the visible bands was developed based on the simplified radiative transfer equation and two assumptions. We evaluated the algorithm using a Landsat8 sub-image of 041/036 (path/row) acquired on 29 March 2014. Thin clouds disappeared visually. With a nearly cloud-free image acquired on 14 April 2014 as the “truth”, the spatial coefficients between the “truth” image and the image before and after the algorithm increased from 0.47 to 0.83 for Band1, 0.55 to 0.82 for Band2, 0.73 to 0.88 for Band3, and 0.82 to 0.88 for Band4. The increase of the spatial coefficients quantitatively indicated the validity of the algorithm.

Using independent component analysis (ICA) coupled with the quality assessment (QA) band of Landsat 8, an approach for thin cloud removal in Landsat 8 operational land imager (OLI) data was developed. After the ICA transformation of the visible, near infrared, short-wavelength and cirrus bands of OLI data, cloud component was identified by the mixing matrix. Then, a cloud mask derived from the analysis of the QA band was formed such that an image pixel with and without cloud cover was delineated. The cloud component and cloud mask were used to remove the thin clouds. Thin clouds disappeared visually within the OLI data. Using another cloud-free image acquired in the previous overflight as the reference, we assessed the accuracy level of the cloud removal. Before and after the cloud removal, the spatial correlation coefficients increased from 0.69 to 0.83 in band 1, 0.75 to 0.86 in band 2, 0.81 to 0.88 in band 3, 0.87 to 0.91 in band 4, and no change in bands 5, 6, and 7 for pixels identified with cloud cover.

A normalized function was proposed as the framework to study the heterogeneity of urban radar targets. The function consisted of three types of scales, the physical size of a target, heterogeneity in dielectric constant, and target geometry. The azimuth direction or azimuth scale, one component of the target geometry was exampled to investigate the influence of azimuth orientations of buildings on radar backscattering. The results should advance SAR application and theory.

The aim was to study seasonal snow and permanent snow variation in alpine regions using coherence component data derived from a multi-temporal of PALSAR InSAR data. With coherence decomposition technique, we obtained the multi-temporal data of temporal-coherence component near Mt. Dagu, China, where vegetated surface, seasonal snow cover or grazing area, and permanent snow cover exist. The variation of temporal-coherence component through time indicated changes of snow cover and status within the grazing zone and permanent snow area or areas above tree line. After the analyses of the temporal-coherence components from January to February, February to April, April to May, and January to May of 2008, we were able to identify snow status and change of snow cover above local tree line. The overall accuracy level greater than 71% was achieved in the identification when compared to those derived from multi-temporal TM images of Landsat 5. The results were promising.

Detailed information about a building and its surrounding is generally contained in hi-resolution synthetic aperture radar (SAR) data. An approach to extract the information and to identify the building with linear features from a single hi-resolution SAR image has been developed. With the strong radar backscattering and shape feature of a building in SAR image, external markers were derived by an OTSU algorithm and a morphology algorithm. Then the minima imposition technique was used to modify the edge-enhanced SAR image coupled with the markers. Finally, the linear building boundaries were obtained using the watershed algorithm. Results of building extract from three types of developed areas were satisfactory.

A resample-based spatial variant apodization (SVA) algorithm for sidelobe reduction was studied for synthetic aperture radar (SAR) and inverse SAR (ISAR) imagery with a noninteger Nyquist sampling rate. The weighting function of every sample in the image domain was calculated with the sample and two adjacent noninteger samples. The noninteger samples were obtained by interpolation in the image domain using sinc function. With the proper selection of two noninteger samples, the monotonic property of the weighting function on each side of the sampling point was preserved. The unequivocal determination of sidelobe suppression was achieved for noninteger Nyquist sampled (NINS) SAR and ISAR imagery. In addition, the lower and upper boundaries of the weighting function under the cosine-on-pedestal condition were extended for further sidelobe suppression and main lobe sharpening. The algorithm was implemented and applied to NINS imagery that is simulated. The algorithm was then assessed for acquired SAR and ISAR images. Improved results have been qualitatively and quantitatively achieved in sidelobe suppression and main lobe sharping in comparison with an existing algorithm.

A space-variant chirp scaling algorithm based on the range cell migration (RCM) equalization and azimuth subband synthesis has been studied to process simulated geosynchronous synthetic aperture radar (GEO-SAR) data. The acceptable order of terms in polynomials for the slant range models in the RCM correction and phase error compensation, division of subband, and suppression of grating lobes of the subbands was investigated. Qualitatively and quantitatively, the method was able to focus simulated GEO-SAR signals well. Finally, the constraint on the spatial extent of azimuth and range dimensions using the algorithm was assessed.

A novel autofocus method for synthetic aperture radar (SAR) image is studied. Based on a quadratic model for the phase error within each sub-area (narrow strip × sub-aperture) after a wide range swath is subdivided into narrow range strips and long azimuth aperture into sub-apertures, an objective function for estimation of the error is derived through the principle of minimum entropy. There is only one unknown variable in the function. With the Chebyshev approximation, the function is approximated as a polynomial, and the unknown is then solved using the method of series reversion. Curve-fitting methods are applied to estimate phase error for an entire scene of the full-swath by full-aperture. Through simulations, the proposed method is applied to restore the defocused SAR imagery that is well focused. The restored and original images are almost identical qualitatively and quantitatively. Next, the method is implemented into an existing SAR data processor. Two sets of SAR raw data at X- and Ku-bands are processed and two images are formed. Well-focused and high-resolution images from plain and rugged terrain are obtained even without the use of ancillary attitude data of the airborne SAR platform. Thus, the studied method is verified.

When the range migration caused by motion error exceeds the range cell resolution, the performance of a conventional phase autofocus approach degrades. In this paper, a new adaptive motion compensation (MoCo) algorithm with the removal of the migration that is nonsystematic has been developed for airborne spotlight synthetic aperture radar (SAR) imagery with high resolution. In the algorithm, the relationship between nonsystematic range cell migration (NsRCM) and phase error was first explicitly revealed after the polar format algorithm resampling. The NsRCM could be readily calculated by coarse but reliable phase error estimation. Subsequently, the NsRCM and the bulk of the azimuth phase error were corrected. After the removal of the NsRCM, degradation of the conventional phase autofocus resulting from sidelobe increase as well as mainlobe broadening was avoided. Finally, a fine MoCo procedure was performed to remove the residual azimuth phase error satisfactorily. Through the analysis of the airborne spotlight SAR raw data with high-resolution and wide-swath illumination, a well-focused imagery was obtained. Quantitative assessment of the image quality was satisfactory. The MoCo algorithm was validated.

Former NASA's Deformation, Ecosystem Structure, and Dynamics of Ice (DESDynl) satellite mission was to provide regional or global biomass carbon stock at regional, national, and global scales. Carbon Monitoring System UAVSAR and LVIS data acquired in August 2009 were used in this paper. In this paper we used Cloude's target decomposition theorem to decompose traditional polarimetric SAR data, and then to estimate biomass. By certification of LVIS derived biomass, the results show that only decomposed scattering elements are not enough for biomass retrieval, but they could improve the accuracy of biomass retrieval. The standard error for only pol method is 66.3Mg/ha, but for both polametric and decomposed data is only 64. lMg/ha.

During the data acquisition of a sliding spotlight or terrain observation by progressive scan (TOPS) synthetic aperture radar (SAR), the steering of the antenna main beam increases the azimuth bandwidth but could result in the azimuth signal aliasing in the Doppler domain. To remove the aliasing, one has used a subaperture method. In this letter, we show a focusing scheme without the use of the subaperture for both sliding spotlight and TOPS SARs. In doing so, we eliminated the obvious increase in data volume or the subaperture division by choosing the pulse repetition frequency that is only 20% greater than the instantaneous bandwidth. The method was incorporated with an available imaging algorithm and then used to process simulated and collected data of the sliding spotlight and TOPS SARs. Well-focused results without aliasing were obtained.

There are two square-root terms in the range history of a return signal from a bistatic synthetic aperture radar (BiSAR). The transfer function for imaging in the 2-D frequency or range Doppler domain using the principle of stationary phase cannot be analytically derived. To address this problem, we approximated the stationary phase of the 2-D spectrum with an expansion of the Taylor series on the azimuth frequency and called the approximation as the derivatives of an implicit function (DIF). After algebraic manipulation, the DIF and 2-D spectrum were obtained for a generally configured BiSAR. With the DIF method, we dissolved one square-root term out of the two for an azimuth-invariant BiSAR, which is particularly advantageous in the implementation of an imaging algorithm. Then, a modified range Doppler algorithm was developed to process the BiSAR data. Promising results were obtained.

For a synthetic aperture radar (SAR) onboard a platform with a rectilinear track, the range history of a point target can be accurately expressed hyperbolically. The track can be curvilinear for a maneuverable SAR platform. The hyperbolic equation becomes inadequate, and an expression with high-order terms is needed. Using the method of series reversion, we derived the 2-D spectrum for the return signal of the curvilinear SAR. There were five independent variables in the spectrum, but available imaging algorithms could only handle three in the focusing using the spectrum. Thus, a variable-decoupling method was developed to reparameterize the initial spectrum so that only three variables remained. After the incorporation of the decoupling method into the chirp-scaling algorithm, simulations of the SAR with a curvilinear track were studied. Promising results were obtained.

SAR image from sea can constantly contain ships and their ambiguities in azimuth and range directions. For maritime applications, the ambiguities are visible due to their strong intensities in a low backscattering background of sea environment. Thus, the ambiguities can be often mistaken as ships and cause false alarms. Many approaches have been proposed for reducing the azimuth ambiguities in single channel SAR image. This paper analyzed scattering mechanisms of the azimuth ambiguities for PolSAR images and proposed a method for detecting ships from PolSAR images. By using eigenvector-eigenvalue decomposition, three eigenvalues can be used to differentiate ship targets, azimuth ambiguities and sea clutter. One C-band JPL AIRSAR polarimetric data have been chosen to evaluate the method. The experimental results show that the proposed method can effectively reduce false alarms caused by the azimuth ambiguities.

An innovative constant false alarm rate (CFAR) algorithm was studied for ship detection using synthetic aperture radar (SAR) images of the sea. Two advances were achieved. An alpha-stable distribution rather than a traditional Weibull or$K$-distribution was used to model the distribution of sea clutter. The distribution of sea clutter in a SAR image was typically heterogeneous, caused mainly by variable wind and current conditions. Image segmentation was carried out to improve the homogeneity of the distribution in each subimage or region. In comparison with ship detection using the CFAR algorithms based on the Weibull or$K$-distribution, our algorithm detected the most number of ships with the smallest number of false alarms.

Floodplain inundation and vegetation along the Negro and Amazon rivers near Manaus, Brazil were accurately delineated using multi-frequency, polarimetric synthetic aperture radar (SAR) data from the April and October 1994 SIR-C missions. A decision-tree model was used to formulate rules for a supervised classification into five categories: water, clearing (pasture), aquatic macrophyte (floating meadow), nonflooded forest, and flooded forest. Classified images were produced and tested within three days of SIR-C data acquisition. Both C-band (5.7 cm) and L-band (24 cm) wavelengths were necessary to distinguish the cover types. HH polarization was most useful for distinguishing flooded from nonflooded vegetation (C-HH for macrophyte versus pasture, and L-HH for flooded versus nonflooded forest), and cross-polarized L-band data provided the best separation between woody and nonwoody vegetation. Between the April and October missions, the Amazon River level fell about 3.6 m and the portion of the study area covered by flooded forest decreased from 23% to 12%. This study demonstrates the ability of multifrequency SAR to quantify in near realtime the extent of inundation on forested floodplains, and its potential application for timely monitoring of flood events.>

Synthetic aperture radar (SAR) data were acquired over Bonanza Creek Experimental Forest (Alaska) in March 1988 under thawed and frozen conditions. For five stands analyzed, L-band backscatter at 42 degrees -45 degrees incidence angle was 2.7-6.9 dB smaller under frozen than under thawed conditions for white spruce and balsam poplar, with the largest difference at HV and the smallest at HH polarization. The differences were smaller for a stand of small black spruce. The VV-HH phase differences observed by SAR were approximately=0 degrees for all the stands. Ground data were used to parameterize the Santa Barbara canopy backscatter model. For the white spruce and balsam poplar stands under thawed conditions, simulations agreed with the SAR data within the calibration uncertainty. The model underestimated the HH, HV, and VV backscatter for all five stands under frozen conditions, and for the black spruce stand under thawed conditions. The modeled VV-HH phase differences were close to 0 degrees for all the stands except the black spruce stand. The discrepancies in model predictions of backscatter and phase difference were attributed to inadequate surface backscatter modeling. Model results supported the hypothesis that the weaker backscatter from frozen stands was because of the smaller dielectric constant of the frozen trees.>

The authors compared the output of the Santa Barbara microwave canopy backscatter model to polarimetric synthetic aperture radar (SAR) data for three ponderosa pine stands (ST-2, ST-11, and SP-2) with discontinuous tree canopies near Mt. Shasta, California, at P-band (0.68-m wavelength), L-band (0.235-m wavelength), and C-band (0.056-m wavelength). Given the SAR data calibration uncertainty, the model made good predictions of the P-HH, P-VV, L-HH, C-HH, and C-HV backscatter for the three stands, and the P-HV and L-HV backscatter for ST-2 and SP-2. The model underestimated C-VV for the three stands, and P-HV, L-HV, and L-VV backscatter for ST-11. The observed and modeled VV-HH phase differences were approximately=0 degrees for the three stands at C-band and L-band, and for SP-2 at P-band. At P-band, the observed and modeled VV-HH phase differences were at least -80 degrees for ST-2 and ST-11, which indicates that double-bounce scattering contributes to the total backscatter for the two stands.>