Hemispherical photography is an important method of leaf area index (LAI) measurement, but the intermediate processes such as image segmentation and clumping index estimation will introduce errors. In this paper, an end-to-end model was proposed to directly estimate LAI from a hemispheric image based on GhostNet, which avoids errors introduced by the intermediate processing. Hemispherical images of crops and forest vegetation obtained from Shihezi, Xinjiang and Xiong'an, Hebei, and the measured values using LAI-2200 were utilized as the dataset. Compared with the LAI-2200 measurement results, the analysis results show that the correlation between them is extremely significant, with$\mathrm{R}^{2}=0.80,\ \text{RMSE}=0.65,\ \text{MAE}=0.46$. The experimental results show that the estimation model based on GhostNet can accurately estimate the LAI of the hemispheric image and is suitable for timely and accurate estimation of the LAI value of various types of vegetation through edge devices.

The fine classification of vegetation is a prerequisite for relevant remote sensing applications. Based on the phenological differences of different vegetation and the temporal changes of vegetation index, a crop fine classification algorithm is proposed, which combines spectral angle mapper algorithm and spectral information divergence algorithm. Taking Shihezi City, Xinjiang Province as the research area, 15 Sentinel-2 high-resolution images are selected as the data source, combined with the local phenological information, the NDVI time series curves of different crops are constructed, the refined classification of crops in this area is carried out, and compared with the traditional SVM algorithm and maximum likelihood method, The results show that the overall classification accuracy of this algorithm is 89.25% and 86.45% respectively, the kappa coefficient reaches 0.79 and 0.78. The experimental results fully show the advantages of the proposed algorithm compared with the traditional single temporal data source algorithm, and have a certain reference significance for the application of remote sensing images in ground feature classification.

Optimal exposure time is essential for accurate measurement of LAI (leaf area index) by DHP (digital hemispheric photography) method, The results showed that LAI was underestimated in different degrees under automatic exposure mode. To address this problem, in this paper we constructed a model of light intensity and optimal exposure time by studying the quantitative relationship between light intensity under the canopy and exposure time. The result shows that using the exposure based on this model rather than the automatic exposure, the comparison of LAI from the LAI-2200 and digital photographs is greatly improved, with R2 increasing from 0.398 to 0.845, and RMSE decreasing from 1.298 to 0.293. The method helps to solve the uncertainty of optimal exposure time in DHP method and improve the accuracy of DHP method in LAI measurement.

In this paper, an unsupervised classification method is proposed for polarimetric synthetic aperture radar (PolSAR) images. The proposed method combines the Freeman decomposition and Cloude decomposition to improve the initial classification take advantage of the scattering mechanism. Decomposition coefficients of three scattering mechanism components are extracted through Freeman decomposition first; then the scattering entropy and angle are calculated by Cloude decomposition. Employing the aforementioned five parameters, the initial classification plane is redefined and divided based on the dominant scattering mechanism. Finally, a clustering algorithm is adopted to the iteration of the initial classification, which optimizes the boundaries between different terrains. The experiment results demonstrate the superiority and effectiveness of the proposed method.

Existing image matching is mostly limited to homologous images, but there are few researches on heterogeneous image matching such as UAV images and satellite remote sensing images. Aiming at the slow speed and low accuracy of traditional image matching methods when matching small-scale UAV images with satellite remote sensing images, a secondary matching method based on image edge features is proposed. The gradient information calculates the normalized cross-correlation formula to obtain the first matching result, and then obtains the optimal matching result through the shape context algorithm. Experimental results show that this method can well overcome the influence of factors such as illumination, low resolution, rotation, etc., and match UAV images and remote sensing images in a short time. The average precision and recall of the algorithm can reach 67.75% and 64.66%. Compared with the traditional SURF algorithm, it has faster matching speed and better matching accuracy.

The fuzzy c-means (FCM) algorithm and many of its variations have been widely adopted for image segmentation tasks. However, these methods are unable to present satisfactory segmentation results when dealing with synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. In order to achieve the effective segmentation of SAR images, a robust FCM algorithm, namely NCBS_FCM, is proposed. The nonlocal spatial information is utilized to reduce the effect of speckle noise. Furthermore, NCBS_FCM takes advantage of the comentropy based on local gray histogram to acquire the adaptive weighting parameter for nonlocal spatial information term, which can achieve a better balance between speckle suppression and edge detail preservation. In addition, this paper incorporates the between-cluster scatter term into the objective function to adjust the distance between the cluster centers accordingly. Therefore, NCBS_FCM is more robust to various images and achieves satisfactory segmentation accuracy. Experiments on simulated and real SAR images show that NCBS_FCM outperforms other proposed variations of FCM by a significant margin.

This paper discusses the temperature factors leading to permafrost instability, and explains the causes of permafrost instability from the perspective of land surface temperature (LST). First, the land surface deformation of the typical permafrost area in some period is extracted by the using the dual-track differential interferometry (D-InSAR) technique with Sentinel-1A single look complex(SLC) data. Next, the annual average LST and LST range are calculated using the spatio-temporal interpolation algorithm for MYD11A2 products. Comparing the distribution of land surface deformation and LST, findings show that permafrost regions with lower average annual LST and larger annual temperature range are more unstable and more prone to surface deformation. This research can contribute to unveiling the process where LST affects the freezing and thawing of permafrost.

In this paper, Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is employed to extract the deformation of the transmission tower foundations. The extracted results are compared with the measured results to verify the correctness of the extracted results. 26 SAR images from January to December 2021 are selected for the deformation extraction of the high voltage transmission tower foundations near the landslide area in Guangyuan City, Sichuan, China. Time sequence analysis was performed using the StaMPS software, and GACOS products were used for atmospheric correction. The final deformation results are compared with the monitoring results of the sensors installed on the foundations of the transmission tower. The comparison shows the effectiveness of the method. The conclusions obtained from the experiment provide scientific guidance and suggestions for the power maintenance personnel.

This paper proposes a landslide detection method combining gray level co-occurrence matrix (GLCM) and polarization decomposition technique. The proposed method makes full use of the characteristics of the SAR image and combines the polarization information with the texture feature. Landslide candidate areas were identified using GLCM and final landslide areas were determined using polarization decomposition techniques. The data used for evaluation is taken from Guangyuan City, Sichuan, China. The proposed method successfully detects the changes of two SLC sentinel-1A C-band VV and VH dual polarization SAR images before and after the landslide, which proves the effectiveness of this method.

The indirect estimation of leaf area index (LAI) is important to agricultural science and ecological science. In this paper, we reconstructed the three-dimensional (3D) structure of the plant based on 3D reconstruct technology, such as the Structure from Motion (SFM) and the multiview stereo (MVS). A LAI measurement method was also proposed based on the ratio of the leaf point cloud area to the ground area. The experiment results showed that the R2between the true value of the leaf area (LI-3000) and the measured value is 0.9657; compared with the true LAI value calculated based on the leaf area, the relative error of the LAI value extracted from the 3D model is about 44.4%. After removing the stems and smoothing the leaves, the R2between the true value of the leaf area and the measured value reached 0.9741; the relative error of the measured value of LAI is 9.13%. The experimental results also implied that the deviation of the LAI measurement value mainly comes from the incomplete reconstruction of the covered part of the plant, the influence of the stem, and the smoothness of the leaf surfaces after reconstruction.

To solve the problem of the unstable feature point extraction and the difficult feature matching in SAR image processing, we proposed a detection algorithm based on hybrid feature extraction. First, the repeated gray information is removed to reduce the complexity and import the real-time performance of the algorithm. Then MSER is utilized to extract regional extremes as candidates. And Harris-Laplace algorithm, which ensures uniform and stable feature-point-distribution, is employed to detect the feature points. Next, an improved SAR-SIFT algorithm is used for the feature matching. Finally, FSC and VFC are devised to refine the feature matching results. The dataset we used is the GaoFen-3 series image as it retains fine edge and structural texture information. The experiments have demonstrated the effectiveness of the proposed algorithm with insert performance metric here.

This paper proposed a novel river detection algorithm for SAR images with an improved version of OTSU algorithm. The width of the river can also be estimated using the detected results. The gray threshold, which is critical in the detection of the river, can be obtained adaptively by the proposed improved OTSU algorithm. Thus, it is more accurate than the conventional OTSU algorithm. In addition, DEM is employed in the river region extraction process to mitigate the effect of mountain shadows. The target refinement, the skeleton extraction and the endpoint removal algorithms are all devised to acquire the river centerline image. And the distance transformation algorithm is applied to generate the river distance map. Henceforth, one can estimate the river width by combining the previously acquired images. In order to create a more constant river centerline image, we employed a bridge removal procedure before the river skeletonization. The data used for the evaluation is from Sentinel 1A single-polarization data of the Yangtze River near Nanjing City, Jiangsu Province. The experiment results have shown that the proposed algorithm is more effective in the river extraction.

Road extraction from remote sensing images based on deep learning has always been a hot research topic. However, due to the complexity of road conditions, many deep-learning models cannot obtain satisfactory results of road extraction. To improve the accuracy of road extraction, this paper proposes an improved deep-learning model based on the structure of Deeplabv3+. The proposed model uses four blocks with ResNeSt and ASPP to extract the feature maps, which can improve the integrity of the extracted roads. And each of the extracted low-level features is transmitted to the decoder. The decoder focuses on the effective fusion of low-level and high-level feature maps and gradually restores this information layer by layer, which can produce a more accurate result of segmentation. And because the road results extracted by neural networks often have broken road lines, this paper also proposes a data post-processing method to effectively solve this problem. The final experimental results show that the proposed model has further improvements in accuracy, mean Intersection over Union, and Fl-score compared to some other state-of-the-art(SOTA) models.

Monitoring aerosol distributions at the city level is of great importance. However, because the differences in spectral response functions and spectral channels among different sensors, the widely used second-generation operational aerosol retrieval (V5.2) algorithm cannot be directly applied to high-resolution Landsat 8 OLI images. In this study, we proposed a new high-resolution aerosol retrieval algorithm for Landsat 8 OLI images over urban areas. This new algorithm retrieved aerosol optical depth (AOD) directly from the red-blue surface reflectance relationship, without estimation surface reflectance. Therefore, satellite measurements even without V5.2-required$1.24\mu \mathrm{m}$or$2.1\mu \mathrm{m}$channel are able to retrieve AODs using this algorithm. For validation, the AERONET measurements located in Beijing were collected. The validation results show that our 30m retrievals agree well with the AERONET measurements, with correlation coefficient of 0.944, EE of 85.19%, MAE of 0.051, and RMSE of 0.065, and are able to provide detailed aerosol distributions over urban areas.

With the further improvement of industrialization, the problem of atmospheric pollution has become a major issue closely related to our human existence that is of common concern to scientists worldwide. The rapid development of remote sensing technology makes it possible to observe the atmosphere over a long period of time and on a large scale. In this study, we compared the Moderate-resolution Imaging Spectroradiometer (MODIS) aerosol product MCD19A2 with the NO2 retrieved by Sentinel-5 Precursor (Sentinel-5P). It is found that the data based on Sentinel-5P can clearly detect the local steel smelting enterprises while MODIS cannot, and the column density distribution trend has a better correlation with the steel output of the current month. If the spatial resolution of related products is improved in the future, this method of atmospheric observation can be better used for pollution monitoring.

SAR image enhancement plays an important role in the process of SAR image processing and information interpretation. Super-resolution reconstruction is a widely adopted enhancement method. However, it is difficult to achieve a decent tradeoff between reconstruction effectiveness and the convergence speed for existing methods. To combat such problem, this paper proposed a novel solution to it, we started from the modeling of SAR image degradation principle, applying the adaptive line search strategy to the SAR image super-resolution reconstruction process, and redefined the step size selection in the reconstruction process, made it possible to achieve both reconstruction effectiveness and convergence speed. Compared with the existing empirical setting or iterative selection, the proposed method can reduce the number of iterations while guarantee the reconstruction results.

The polarimetric synthetic aperture radar (PolSAR) image filtering is an essential step before the quantitative inversion. However, the existing filtering algorithms often change the scattering features of the original PolSAR images. This leads to a performance decrease for the subsequent quantitative inversion. In order to combat such drawback, we adopted a novel filtering algorithm preceding the quantitative inversion. This algorithm adopted the idea of “classify first, filter later”. By utilizing the hybrid four-component polarization decomposition (HPD) method to pre-classify the pixels, and implementing non-local means lee (NLM-Lee) filtering only between the same ground object points, the scattering features are well-protected. The experiment results show that the proposed algorithm can not only effectively reduce the speckle and preserve the structural features of the image, but is far superior in preserving the scattering features of the images. Hence, when the PolSAR image filtering is carried out by the proposed algorithm, the performance of the quantitative inversion will be improved.

In this work, an effective 3-D reconstruction method for the transmission tower in mountain area, which combines the Persistent Scatterer Interferometry (PSI) and SAR Tomography (TomoSAR) based on generalized Orthogonal Matching Pursuit (gOMP) algorithm, is proposed. This paper is focused on reconstruction performance of gOMP algorithm on SAR signal and the 3-D reconstruction of transmission tower, using the results obtained in mountain area on simulated and real TerraSAR-X data. The vertical signal of slant distance from SAR images can be regarded as sparse signal, so the gOMP algorithm can be used to obtain extraction of elevation and deformation rate of transmission towers in mountain area. When only primary and secondary scattering points is present in each range-azimuth resolution cell, this method can be exploited to estimate the elevation and the deformation rate of the transmission tower. The results have demonstrated the advantage of this method.

SAR image enhancement is of great significance to SAR image processing. Aiming at the defects of traditional partial differential algorithms sensitive to noise, this paper proposes a SAR image enhancement method using both P-M nonlinear equations and coherent enhanced partial differential equations. Combining the physical model of the SAR image, the generalized diffusion process synthesizes the properties of both forward and backward diffusion, and has a good smooth enhancement effect at edges as well as smooth areas of the image. This hybrid model not only avoids noise enhancement, but also enhances image edges. Experiment results show that the proposed hybrid model has significantly improved the sharpness of enhanced SAR images with minimal distortion in image details, which is proven to be superior than applying only single one of the models.

Aerosol distributions may change at fine spatial scales in urban areas due to building and transport infrastructure and human population density variations. The recent availability of Sentinel-2 satellite data provides the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. In this study, a novel high-spatial-resolution AOD retrieval algorithm for bright unban surfaces was developed based on the Sentinel-2 images. The surface reflectance for AOD retrieval was estimated from the image that has the minimal aerosol contamination in a temporal window. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements ( R=0.9424), with 85.56% of them falling within the Expected Error (EE). The mean absolute error (MAE) and the root mean square error (RMSE) are 0.0688 and 0.0882, respectively. These results suggest that our high-resolution AOD retrieval algorithm is robust and useful to retrieve high-resolution AOD over bright urban surfaces based on Sentinel-2 images.

In this paper, an improved change detection method (ICDM) is proposed to obtain the soil surface moisture (SSM) with a resolution of 20 meters, which greatly improves the resolution of SMAP soil moisture products. First, NDWI was calculated using a time series data of Sentinel-2 and then converted to vegetation water content (mveg). With the help of the water cloud model, the influence of vegetation on the backscattering coefficient is eliminated. Then, in order to make the change detection method (CDM) more reasonable, a method for removing influence of roughness change on backscattering coefficient is proposed. It uses the difference between co-polarization and cross-polarization ( σ0vv- σ0vh) for roughness normalization. SSM was obtained by combining the soil relative moisture retrieved from the time series data of Sentinel-1 with the maximum and minimum water content of the study area within one year obtained from the SMAP data. The results showed that the MRE of SSM extracted by ICDM was 8.34%, which was less than 17.49% of CDM.

In this paper, a landslide detection method using Small Baseline Subset Differential Interferometry (SBAS-DInSAR) combined with gray level co-occurrence matrix (GLCM) that combines the surface deformation rate with the texture feature information of the ground object to make full use of the ground object information. Compared with the traditional technique using only texture features, taking Qinglong County, a mountainous region in Guizhou Province in the southwestern region as the research area, can improve the accuracy of landslide detection. The final experimental results demonstrate the effectiveness of the proposed method, which can be used for landslide warning, the general survey of landslide bodies, and provide auxiliary information support for the detailed investigation.

Detecting the change of texture information is very important for landslide detection. In polarimetric SAR imagery, the landslides not only have typical polarimetric features but also have rich texture features. In this study, the texture feature extraction technique based on gray scale co-occurrence matrix was applied to process two SLC Sentinel-1A C-band SAR images and two COSMO-SkyMed X-band SAR images acquired before and after landslip for the landslide detection leading to the Jomda landslide event in 2018, Tibet of China and the Qinglong landslide in 2018, Guizhou of China. Several important characteristic parameters of the gray scale co-occurrence matrix were studied, and the optimal sliding window size and the optimal step size for landslide detection were obtained through rigorous analysis and comparison. The experimental results show that the analysis of the complex texture information can effectively detect the landslide in the mountain area with high accuracy.

In this paper, to address the problem in the registration of synthetic aperture radar (SAR), the speeded up robust features (SURF) approach based on multi-level FAST and improved random sampling consistency is proposed. This approach is used to address the high mismatch rate in SAR image registration, and it is based on the characteristics of SAR images. We construct a new corner detection by combining with the hierarchical theory of the system, which is referred to as multi-level FAST (MFAST). The contribution of MFAST algorithm mainly lies in the efficiency of SURF algorithm, which determines the main direction and feature descriptor. And improving the random sampling consistency (RANSAC) to remove mismatched feature points and ameliorate the registration effectiveness. The proposed algorithm is suitable for multi-sensor images with large gray differences and significant edge features. The experimental results show that the registration efficiency of the proposed algorithm is more than three times of the SURF algorithm, and the registration accuracy also is better than traditional SURF algorithm, which can reach 0.39 pixels.

For the measurement of LAI (Leaf Area Index) by DHP (Digital Hemispherical Photography) method, imprecise segmentation is the key error source. In this paper, to our knowledge, a deep learning algorithm is used for the first time to segment upward hemispherical image of vegetation. Pix2pix, a general mapping learning model, was improved in our study to make it more suitable for processing segmentation problem. Thousands of images collected in the field were labeled to train the model, and the conventional methods based on pattern recognition, such as the Otsu and HSV, were compared. The result shows that the improved pix2pix algorithm significantly improved the accuracy of the segmentation, which reached to 0.9834. Furthermore, this model has a good performance in processing pictures of complex environments, and the segmentation of edge details has also been optimized. Those results show that the method has great potential to improve the LAI measurement accuracy.

The popular enhanced deep blue (DB) algorithm, though performs well in aerosol retrieval over the entire land surfaces, limited by the low temporal resolution and insufficient satellite-derived products of most high resolution satellite sensors, is hardly to be applied to urban areas using high spatial resolution satellite imageries. In this paper, we developed a simplified deep blue algorithm to retrieve 30m spatial resolution aerosol optical thickness (AOT) over urban surfaces using Landsat 8 OLI measurements. With a few atmospheric correction surface reflectance-apparent reflectance pairs, robust relationships between visible (0.65μm and 0.48μm) and 2.2μm reflectance can be constructed by this algorithm. Difficulties described above are overcame, and high consistency with ground-based AERONET measurements are achieved over Beijing, with correlation coefficient (R2) ~0.951, root mean square error (RMSE) ~0.005, mean absolute error (MAE) ~0.05 and 82.61% retrievals fall within the expected error (EE) envelop. This study demonstrates that the simplified deep blue algorithm has advantages in retrieval high spatial resolution AOTs over urban areas.

Differential SAR tomography (D-TomoSAR) extends the SAR tomography to the time dimension for four-dimension imaging. Hence, it is a powerful tool for retrieving the four-dimensional structure of buildings in urban area. D-TomoSAR can also be applied to electric power transmission tower evaluation. However, among the previous D-TomoSAR estimators, some reduces the azimuth-range resolution and has large sidelobe while the others cost more computation to retrieve. To overcome aforementioned drawbacks, this paper proposed an improved Stagewise Weak Orthogonal Matching Pursuit (SWOMP) based on twice iterations and scatter number estimators to calculate the height and deformation velocity of the electric power transmission tower. Through simulations and real data (TerraSAR-X) experiments, a comparison between the SWOMP, adaptive beamforming (Capon) and compressive sensing (CS) was carried out. The results indicate that the proposed method obtains less sidelobe than SWOMP and Capon. In addition, this method obtains similar result to CS in less computation time. the results have demonstrated the advantages of this method.

An improved deramping method which is an essential part of differential tomography SAR (D-TomoSAR), is proposed in this paper. Based on the time series of SAR data, first the coherence coefficient method is employed for image registration. Next, the image center is chosen as the reference point. The reference point's exact coordinates can be calculated by Gradient-descent algorithm to solve the Range-Doppler (R-D) equations, and it converges when the difference between the calculated elevation and the one from DEM database meets the threshold requirements, The experiment results have demonstrated that the elevation estimation of the target tower using deramping method proposed in this paper is closer to the real height of the tower by Yunnan electric power bureau compared to least squares method. Furthermore, the accurate deformation rate of the transmission tower can be extracted by this method.

Conventional measurement methods with low spatial resolution, differential interferometric synthetic aperture radar (D-InSAR) is susceptible to signal decorrelation and atmospheric delay, so InSAR time series analysis is widely used for detection and monitoring of slow surface deformation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is based on a large number of SAR images, but small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) only needs a small number of images and performs better than PS-InSAR for obtaining nonlinear deformation information. In this paper, 21 COSMO-SkyMed radar images acquired between 2016 and 2017 are processed for land subsidence monitoring with the Small Baseline Subset (SBAS) approach in Yongshan County transmission corridors in China.

In order to accurately extract landslide disaster information from SAR images, we propose a clustering algorithm for landslide detection using multi-temporal fully polarimetric SAR image. By combining Freeman-Durden decomposition with H /α / A decomposition method, this algorithm makes full use of scattering power and scattering entropy information. Furthermore, a hierarchical method is proposed to overcome the drawback that fuzzy membership function family of multi-region classification cannot always satisfy the constraint conditions to guarantee functional convexity. Such method ensures that the constraint conditions can always be satisfied and improves the accuracy of classification. Finally, the landslide disaster area is accurately extracted from the two classification maps by change detection. The results of the C-band RadarSat-2 data are provided to demonstrate its competitive general performance of image classification.

Resolution is one of the important indices to measure the quality of SAR images. Super-resolution reconstruction is a widely adopted resolution enhancement method. Many algorithms have been developed for the super-resolution reconstruction. Among these algorithms, this paper applies projections onto convex sets algorithm to SAR image reconstruction processing. The POCS can efficiently obtain high-resolution SAR images with enhanced details. However, the POCS requires many low-resolution SAR images of the same area to gain a better result, usually 10 to 20 images. Such requirement is very difficult to achieve when only single-polarization mode is included. In this paper, we propose a novel method that utilizes all the polarimetric images of the same original SAR data for the algorithm. Thus, the number of the available images is increased exponentially. The experiment results have demonstrated the effectiveness of our proposed method: The reconstructed high-resolution SAR image based on multi-polarimetric information is more detailed and clearer than that based on single-polarization information.

In order to accelerate the calculation of the similarity metric between two patches in non-local means filter of polarimetric synthetic aperture radar (PolSAR) images, this paper proposes a weighted acceleration algorithm to eliminate redundant operations. It can greatly reduce the computational complexity by using the accumulated covariance data to avoid repeated computations. Furthermore, the algorithm introduces the polarization similarity parameter to preserve the details of the image. It also processes the covariance matrix of each pixel. Thus, the polarimetric properties are maintained. The results of the C-band RadarSat-2 data are provided to demonstrate its rapidity and competitive general performance of speckle reduction and edge preservation.

Tomography SAR (TomoSAR) imaging can distinguish different scatterers in the same pixel using their spatial frequency spectrum difference. Sparse scatterers case located in one pixel can be imaged using high resolution spectral estimation algorithm or compressed sensing (CS) algorithm. For the pixel where the top position of the electric power transmission tower is located, it can be seen this sparse scatterers case. In this paper, TomoSAR imaging methods based on MUSIC algorithm, ESPRIT algorithm and CS algorithm are used to calculate the height of the electric power transmission tower, respectively. TerraSAR-X data is used for experiment. The experimental results demonstrate the ESPRIT algorithm has better accuracy than the other two algorithms for the height estimation of the study electric power transmission tower. Basically, this accuracy can meet the requirement in practical applications.

Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterers interferometry (PSI) and small baseline methods(SB), which are optimized for different models of scattering. However, the scattering characteristics of transmission corridors in mountain areas usually lay between these two end-member models. In the experiment, we selected a set of SAR images of TerraSAR-X satellite, covered Mao County area, to detect the subsidence during the fifteen months, using stanford method for persistent scatterers-multi-temporal InSAR technology (StaMPS-MTI).

The complex terrain, frequent movement of the earth's crust, dense vegetation exists in the western sichuan plateau where has frequent disasters. PSInSAR can extract the target with stable, strong scattering characteristics to monitor deformation effectively. The experimental area is in maoxian, erlang- mountain, chengdu. Paper uses, Pseudo-3D Phase Unwrapping and LAMBDA Method respectively related to GPS integer ambiguity resolution to unwrap phase and extract the surface deformation of the research area, and comprehensively analyze the effects of different surface and different solutions to the results. Experimental results show that the Network Adjustment Method in elevation correction and linear deformation rate has a comparative advantage, can be more applicable to maoxian, mountain complex mountainous area surrounding the transmission channel of deformation monitoring.

The inclination direction monitor of the power transmission tower can issue an early-warning for the risk of the tower collapse, as well as the collapse direction. In this paper, we proposed a new method to retrieve the inclination direction of the power transmission tower using geocoding. First, the backscattering coefficient was utilized to separate the power transmission tower from the background, and figure up those coefficients along the tower direction to extract the main body of the tower. Second, the whole image was traversed by a$3^{\ast}3$template window to refine and mark the most possible learning tower points. Finally, a distance-Doppler model was built to retrieve geographical coordinates of those marked points. According to the geographical coordinate of the tower point at different time, we got tower inclination direction. Compared with the actual measurement results of the study area, the retrieval result has a 0.0025° error, and the direction is north-east, consistent with the actual direction.

In the electric power transmission corridor areas, the ground surface deformation will influence the inclination of the electric power transmission towers even after the collapse of the towers. Due to the layover, traditional differential synthetic aperture radar interferometry (DINSAR) cannot be used to obtain the inclination displacement of the general line shape and vertical coherent objects such as electric power transmission towers fixed vertically in the ground. Based on the improved DINSAR (IM-DINSAR) model, this letter proposes a revised implementing algorithm for IM-DINSAR model. It can remove the top-bottom vertical-height interferometry phase of the tower (caused by the height of the tower itself, similar to the flat earth interferometry phase in traditional DINSAR) by only using the information of every tower itself. This is different from the implementing algorithm proposed. Then, we use the residual differential interferometry phase after phase unwrapping to obtain the inclination displacement of top position of the tower. Moreover, this letter analyzes atmospheric phase, noise, and multitemporal series cases in IM-DINSAR model. Simulation results demonstrate the effectiveness of the model proposed in this letter.

Synthetic Aperture Radar (SAR) image segmentation is a difficult problem due to the presence of strong multiplicative noise. To attain multi-region segmentation for SAR images, this paper presents a parametric segmentation method based on the multi-texture model with level sets. Segmentation is achieved by solving level set functions obtained from minimizing the proposed energy functional. To fully utilize image information, edge feature and region information are both included in the energy functional. For the need of level set evolution, the Ratio of Exponentially Weighted Averages (ROEWA) operator is modified to obtain edge feature. Region information is obtained by the Improved Edgeworth Series Expansion (IESE), which can adaptively model a SAR image distribution with respect to various kinds of regions. The performance of the proposed method is verified by three high resolution SAR images. The experimental results demonstrate that SAR images can be segmented into multiple regions accurately without any speckle pre-processing steps by the proposed method.

Vegetation coverage is an important indicator for forecasting geological disasters in mountainous areas such as landslide. However, it is a challenge to extract vegetation coverage in complex terrain from SAR image. A major problem is that the variation of the backscatter coefficient of the same object varies with the local incidence angle. As a result, a large number of discrete points appear in the classification results. Focus on this problem, a region-based method of vegetation coverage monitoring has been presented in this paper. Based on the results of yamaguchi decomposition and SVM algorithm, the Watershed algorithm is used to over-segment the image and regions are merged on a pixel-voting basis. When the modified method was applied on RADARSAT-2 data, the research results show that the completeness and correctness are improved compared with the method based on yamaguchi decomposition and svm algorithm.

This paper made a coherence analysis to access the ability of snow extraction based on the Terra-SAR X-HH data, and develop a method using multi-temporal coherence combined with the traditional method. Two pairs of Terra-SAR data with the same temporal baseline and similar spatial baseline were chosen, before and after snowfall. Traditional method was used to remove the snow free area with relative higher coherence. Differences between two coherence data was taken to distinguish snow covered from the snow free area which shows low coherence value under X-band condition. Backscatter coefficient data was used as well. The result was verified using GF-1 optical image. An accuracy of 82.44% was achieved if we consider the result from GF-1 optical image as `ground true'. Google Earth image was also used as verification which shows a good estimation of the snow covered areas.

In this paper, the dynamic programming algorithm (DPA) is firstly proposed to analyze microwave minor scattering from advanced Michigan microwave canopy scattering (MIMICS). According to the decision stages which divided by the incidence angle, we use the DPA to study the underlying submerge information under the vegetation. Simultaneously, constructed in the DPA, the multidimensional sequence of state and recursive strategy is derived from the incident angle and polarization. Thus, submerge in the underlying surface can be effectively distinguished by the accumulation of minor scattering. The backscattering coefficients are simulated from the Michigan microwave canopy scattering (MIMICS), and the result of simulation is consistent with the conclusion of ground-based radar scatterometer (GBRS) experiments. Finally, field experiments are carried out and the observation is used for validation.

In this work, we have explored a new method which based on Bayesian assimilation for monitoring methane (CH4) emissions at different rice phenological stages. Specifically, we investigate two algorithms, one based on the ground-based radar scatterometer (GBRS) with full polarization (HH, HV, VH, and VV) and the other based on the mechanistic process of agricultural model concluded the parameters of climate, soil and management. Experiments are conducted on conventional static box and results are compared to the outputs of Bayesian assimilation, microwave model, and Denitrification-Decomposition (DNDC) model, respectively. The result shows that the fusion has combined the tendency of mechanistic model and accuracy of microwave model. For the truth and authenticity of data and result, three criteria indicate the result with high accuracy not only agree well with the sample value but also provide high reliability on the real methane emissions process over time and space.

In order to study the coherence characteristics of low-backscattering objects such as roads and rivers, we introduce a coherence estimation approach based on clustering method. When the approach is applied to multi-temporal high-resolution TerraSAR-X images in urban areas, the results show that the coherence of roads is higher than that of rivers and shadows. This indicates that coherence features can be used to distinguish roads and water bodies. Further, this paper proposes a road detection method of synthetic aperture radar (SAR) images based on path operators and support vector machine (SVM), which combines the backscattering and coherence characteristics of the roads. Experimental results show that coherence features can be used for road detection.

This paper reports on an experiment conducted at the wind-wave tank in UESTC microwave chamber to characterize the C- and X-band radar return from water surfaces covering oil films when observed at different incidences. The measurements of Normalized Radar Cross Section (NRCS) with bi-objective calibration technique were carried out for full polarization and various wind speeds. Comparisons are performed with clean sea water and diverse oil spills. From this data set we validate the Two-Scale Model (TSM) as a calculating method to simulate the backscatter coefficients of oil spill surface. The applicability of experimental results to SAR image extraction is discussed.

The paper aimed to investigate interaction between electromagnetic wave and cereal parameters at column and row directions. S-band (center frequency 3.2GHz) and wheat had been selected as the research targets. During an entire wheat growth cycle, a series of data related to the interaction were collected for the analyses. Backscatter at row and column directions were analyzed as the function of the wheat temporal variations parameters. The research results show the dominated contribution determined the influence of wheat at row and column directions on radar backscatter. Moreover, a simple model has been presented to minimize the influence of column and row directions on radar backscatter. The research can be helpful to modeling and cereal monitoring by microwave remote sensing technology.

Persistent scatterers synthetic aperture radar interferometry (PSI) is a powerful remote sensing technique to detect the subsidence and landslides with an accuracy of millimeters. Distributed scatterers (DS) can be extracted to increase the measurement points with preserved Persistent scatterers (PSs), especially in non-urban areas covered with vegetation. In the experiment, we selected a set of SAR images of Radarsat-2 satellite, covered Mao country area, to detect the subsidence during the six months. An improved method has been presented to optimize measurement points for applying the InSAR technique to monitor the deformation of mountain areas through processing small scales full-polarization SAR Images.

Traditional DINSAR method is just available to obtain the deformation of no layover areas. While, for some line-shape objects located vertically over the ground, such as power transmission tower, traditional DINSAR technique can not be used to obtain the inclination displacement of it. This paper proposes an improved DINSAR (IM-DINSAR) method which can solve this problem by removing the top-down vertical height phase and obtaining the differential phase just caused by inclination displacement. The Radarsat-2 high resolution spotlight mode data are used for experiment and we obtain the inclination displacement of the towers in the study area based on this new method.

In this paper, a method for road detection based on Duda and path operators has been presented. The roads are represented as slender dark regions with constant width and reflectance in the high-resolution SAR images. The path operators (path openings and closings) were performed as morphological filters in retaining linear structures. However, the filters were not sensitive to the width of linear feature. Focused on the limitation of the method, a preprocessing procedure using Duda operators was introduced before adopting the method of morphological profiles with path operators. When the modified method was applied on RADARSAT-2 datasets for different areas, the research results show that the completeness and correctness are over 70% for road detection from SAR images.

Flood is one of serious natural disasters in the word. Synthetic aperture radar (SAR) has become a popular tool to detect the flood disaster for its distinct benefits such as retrieval of surface information, penetrability, and availability in all weathers. This paper aims to analyze microwave scattering characteristics of soil from low water content to semi-flooded status based on ground-scatterometer radar measurement. The research can demonstrate the scattering characteristic of soil at different status and be helpful to retrieve and monitor flood areas in the disaster. A regressive model combined with the radar data and the submerged proportion of soil has been presented.

The detection of ocean oil spill based on synthetic aperture radar (SAR) image has been a hot topic attracting extensive attention. In this paper, a hybrid scheme, in which we extract feature parameters and then achieve classification as follows, is presented. Two-dimensional (2-D) Otsu algorithm is applied in image segmentation process, and neural network is applied in classification course. Before image segmentation, a sort of universal processing is used, and it enables 2-D Otsu algorithm to be more applicable to SAR images of ocean oil spill.

This letter investigates the influence of row wheat for azimuthal look angles on radar backscatter at L-, S-, C-, and X-bands. The radar backscatter was collected with full polarization (HH, HV, VH, and VV) and incidence angles (10°-70°) at different wheat phenological stages. Simultaneously, wheat parameters (biomass, canopy height, stem density, leaf inclination, etc.) and soil parameters (moisture and roughness) were measured to explain the influence based on radiative transfer theory. The research results show that, when the contribution of soil scattering dominates in total backscatter, the influence of row wheat on radar backscatter is varied with the electromagnetic wavelength and the visible soil contained in radar footprint area. When volume scattering contributes more in total backscatter, the influence of row wheat on radar backscatter mainly comes from leaf parameters and wheat geometric shape. Moreover, research results also show that azimuthal look angles affect radar backscatter much mainly for variable scattering cross section and leaf parameters. The research is helpful for cereal monitoring, modeling, and cereal parameter inversion from synthetic aperture radar images.

The traditional methods for coherence optimization in the framework of multibaseline (MB) polarimetric differential interferometric synthetic aperture radar (DInSAR) applications, such as Best, MB1 equal scattering mechanism (MB1-ESM), suboptimum scattering mechanism (SOM), and exhaustive search polarimetric optimization (ESPO), all have some disadvantages. The MB2-ESM method just can be preferred for its normal accuracy and fast computing time. Signal-noise ratio optimum (SNR-OPT) in the coarse grid followed by the conjugated gradient method (SNR-OPT-CG-CGM) can be selected because of its higher accuracy and acceptable cost time. SNR-OPT has higher computational efficiency compared with ESPO because it makes the 4-D coherence optimization problem transform into two independent 2-D optimization problems (“2 + 2” optimization problem). However, SNR-OPT still costs much time. In this letter, we propose a new method which can further make this “2 + 2” optimization problem transform into one 2-D and two independent 1-D optimization problems (“ 2+1+1” optimization problem). Thus, the computational efficiency will be improved much more compared with SNR-OPT, and meanwhile, the accuracy just decreases a little. Seven full polarimetric RADARSAT-2 images are taken for experiment, and the results also show that the improved SNR-OPT-CG-CGM method is a better method considering the tradeoff between computation time and accuracy compared with other methods for DINSAR applications.

The research made an adaptation for Michigan Microwave Canopy Scattering (MIMICS) model at four bands (L, S, C, X) after taking wheat ears as the first layer. When wheat ears appears in heading stage, they locate on the top of the whole wheat and owes the different dielectric constant and water content, which should be considered the important scattering elements. The scattering character of wheat ears are fully considered in two growth stage (heading stage and ripening stage). In the process of adaptation, the stem and leaves layer was taken as the second layer instead of the trunk layer, which is different from forested environment to cereal condition. A new contribution to backscatter was inserted to compensate the total backscatter. The results after comparing the measured data and the predicted data by adaptation of MIMICS model showed the inclusion of wheat ears as one of the model components is feasible in modeling the wheat backscatter.

This paper developed a method of validation test research of domestic satellites LST products. The experimental data include high resolution landsat8 image, low resolution FY-3B and MOD11A1 LST product. The main steps of the method are as follows: (1) Three different algorithms for retrieving land surface temperature on landsat8 data. Through the comparative analysis of the results, we select an algorithm for subsequent verification. (2) Use the inversion result and MOD11A1 product to compare with FY-3B product respectively.(3) Use matlab to compute some parameters and curve data fit. The study results show that the FY-3B temperature product is similar with the others. It is available in a certain extent.

A new extension method in the equal scattering mechanism (ESM) from single baseline to multibaseline (MB) for polarimetric synthetic aperture radar interferometry (PolInSAR) coherence optimization is proposed in this letter. However, despite this, this new method and the traditional available methods such as Best, ESM, MB-ESM, and suboptimum scattering mechanism have their disadvantages in the framework of differential interferometric SAR (DInSAR) applications. The ESM method cannot guarantee the global maximum in theory and just optimizes an approximation formula of the original coherence definition, which leads to a deviation. The method using exhaustive search polarimetric optimization (ESPO) must search four parameters one by one in a defined step size and cost the main computational drawback. Focusing on the disadvantage of these methods, this letter proposes another new method to transform the 4-D optimization problem into two independent 2-D problems, which costs less time than that by ESPO with basically the same accuracy. Seven full polarimetric RADARSAT-2 images are taken for experiment, and the results show that this new method has a better effect in computation time and accuracy for DInSAR applications.

Road is a great part of transportation, emergency response and disaster relief, the monitor and real-time evaluation of the state of road are always important for the rescue works after disaster. SAR has some advantages of all-weather and all-time, which can be well adapted to disaster conditions. Until now the extraction of road damage information has been studied more in optical remote sensing, while less in SAR images. Base on the researches in optical and the studies of road information extraction in SAR, we explored the road damage information extraction in this paper, and proposed a new method of road damage information extraction.

This paper developed a method of mapping wet snow and dry snow in alpine terrain based on multi-polarimetric and mono temporal ASAR data in the snow melting season.14 ASAR images were used to obtain the variation of the backscattering coefficients of snow covered areas in the snow melting process. Shi's method combining intensity and polarization property is taken as a reference to retrieve wet snow. DEM data was also used to retrieve dry snow. In order to verify the accuracy of the result, we made a scale conversion to images resulting from the ASAR data to match with the MODIS image and found an overall accuracy of 82.04%. The Google Earth image was also used as an aided verification. The results are all found to show a good estimation of the snow covered areas.

Methane is an important greenhouse gas (GHG) in the atmosphere. Conventional methods with Optical and infrared technology still have some questioned in large-area and time. With its advantage of acquiring geo-information in timely, speedy, all-day and all-weather over large areas, microwave remote sensing is one powerful approach in monitoring methane emissions to conventional methods. Ground-based scatterometer was employed to multi-wave and multi-polarization measurement to obtain the microwave scattering coefficients. The methane emission was analyzed by the microwave scattering coefficients and the parameters of the vegetation in observation area. Besides, RADARSAT-2 images are ordered to offer data support to the observation of methane emission from large-sized wetlands. Additionally, the comparison of this experiment results and former experiment results of methane emission from paddy land was done. The distinction of the two significant methane emission sources is analyzed in the paper.

Primarily the article briefly introduces the basic concept of the flat earth removal and baseline estimation, and mainly introduces the method of flat earth removal and baseline estimation based on orbit parameters using Radarsat-2 image, in detail represents the processing of flat earth removal based on orbit parameters. By removing the flat earth phase of the interferogram and analyzing the result of flat earth removal, the accuracy of generated digital elevation model and surface deformation, it shows that the method is effective, thanks to the highly precise orbit parameters. Meanwhile, the baseline estimation can be operated, which improves the efficiency.

The paper proposed a soil moisture retrieval model with S-band field experimental data and volumetric water content measured in field. Focused on the problem that antenna irradiated region may not meet the minimum radar resolution pixel, the research applied the multiple independent samples measuring method and analyzed the relevance between soil moisture and backscattering coefficient of S-band VV polarization. The inversion equation obtained was applied to inverse soil moisture from SAR (Synthetic Aperture Radar) S-band images of HJ-1C (a satellite designed for environment and disaster monitoring). The inversion results were verified by the multiple independent samples data measured and agreed well with the experimental data, which shows the S-band VV polarization data can be used to monitor the soil moisture in a large scale.

A monitoring method of methane emission from rice paddies is developed by using quad-polarization radar datasets of ground-based scatterometer and space-borne RADARSAT-2. The backscattering coefficients in eight different rice growth periods are measured by using a ground-based scatterometer system, and the rice biomass, leaf-area index (LAI), water depth and CH4flux are collected by randomly sampling. Meanwhile, four scenes of RADARSAT-2 quad-polarization synthetic aperture radar (SAR) images covering the same study area are acquired. An experience rice parameter inversion model and a methane emissions model are established according to the measured data. The Models are applied to RADARSAT-2 image after pretreatment and classification to retrieve the rice biomass and the CH4flux at four dates, and come to the total methane emission, eventually. The methane emission per hectare is about 418.88 kg; it shows that methane emissions remain at a low level in the Chengdu Plain.

This paper presents a technique for the detection of damaged road in spaceborne synthetic radar (SAR) images. Roads in SAR image can be modeled as line structures, and are extracted from image by Duda detector, and the roads are accurately detected by removing line structures which aren't road information. We use a change detection algorithm based on Edgeworth approach and Kullback-Leibler divergence to obtain change information of images. Finally, we combined information of road and change detection result for detecting damaged road sections. This technique is applied on RadarSat-2 images that have a resolution of about 3m. The experimental results show that our method can detect mainly damaged road sections.

This paper presents the backscattering coefficients of rice fields using an L, S, C and X-band scatterometer system during the growth period for a rice field. The system has full-polarizations (vv, vh, hv & hh) and can view various incidence angles (0°~90°) and azimuth angles (0°~360°). The field measurements were performed in Qionglai County of Chengdu (China) for the 2009 rice growing season. The rice parameters, including biomass, leaf-area index (LAI) and canopy structure were also measured experimentally in the field. The full-polarizations backscatter measurements at L, S, C and X-band have been analyzed as a function of the incidence angle, and the temporal variations of full-polarizations at four selected 35° incidence angles have been compared with the temporal variation of rice biomass and LAI. The results indicate that the backscattering coefficients have a strong correlation with the biomass and LAI, especially in the L and S-band. Therefore, the ground-based scatterometer is an effective tool for estimating rice growth.

This paper measures backscattering coefficients of wheat fields using L, S, C, X-bands scatterometer system during a wheat growth period. The system has full-polarizations (vv, vh, hv, hh) and can view in various incidence angles (from 0° to 90°) and azimuth angles (from 0° to 360°). The wheat field locates at Qionglai County of China is measured during the 2011 growing season. From November 2010 to May 2011, twelve experimental acquisitions (ground and radar data) are carried out over the test field located in a flat area. At the same time, wheat biomass, canopy structure, LAI, soil parameters and related eco-physiological canopy variables are collected. The temporal variations of each band at selected incidence angles have been analysis during the wheat growing season. The correlation coefficients have been computed between the measured and model estimated values of σ0. The results show that the backscattering coefficient is sensitive to the biomass and LAI.

Hydrographic information is very important. The all-weather and penetrability capabilities of SAR make it useful for water monitoring. Previous research mainly focus on delineate the water boundary, to treat the target as water or no-water area. However, there are some places they ignored, in this paper we call it as semi-flooded area, like lakebed, areas after waters receded and semi-flooded farmland, even some special situations like ruffled water surface. Here we use the maximum between-class variance method (Otsu) combining the characteristics of water backscattering coefficient to define the targets as water, semi-flooded and no-water area in the image. The result shows this method is much better than traditional threshold classification.

With the ENVISAT-ASAR Alternating Polarisation mode and the parameter sets of HJ-IC-SAR as the research foundation, using the measurement data by multi-wave bands and multi-polarization ground-based scatterometer, the paper studies an inversion method of bare-surface soil moisture. The studies simultaneously consider the influence which include root-mean-square height S and correlation length L in this method, and combine the two roughness parameters, expressed as a combination of roughness Zs. Using Integral Equation Model (IEM) to analysis the relationship between Zs in different incident angles and the difference of the backscattering coefficients under two wavelengths, establishes the multinomial semi-empirical model for soil moisture inversion under different angles. The model's validation is conformed by the ground-based scattering datas, the results show that, for the medium and low roughness of the bare surface, the error is smaller than 15 percent between inversion value with the model and the measured soil moisture.

This paper aims to investigate the relationship between microwave backscatter signatures of different bands and polarizations and paddy field parameters so as to accumulate data for accurate inversion of these parameters. In the entire rice-growing season we altogether carried out eight experiments in three paddy fields separately and got a great amount of data of four frequencies(L,S,C,X), and three polarizations(HH,VV,VH). A wide range of ground parameters, such as leaf area index (LAI), biomass (fresh weight), canopy height, stem density, roughness and so on, were measured periodically through the season. In this paper, analyses based on statistical correlation showed that the lower-frequency bands (L, S) VH and HH polarization were highly correlated with LAI, biomass and canopy height while VV polarization was poorly correlated. But the highest correlation coefficient with stem density was found in S band VV polarization. Polarization difference had high correlation coefficients on some unique conditions. Besides, we did some research on the matter that whether the paddy field was flooded.

The S-band and C-band FM-CW land-based radar scatterometers were used to measure the backscattering coefficient of a variety of typical earth substances in northwest of China, including: bare soil, frozen soil, bulrush and maize etc. under the different time and different wave band and different polarized condition. First of all, the measurement principle and performance parameters of scatterometer were introduced, the detailed experimental plans and measurements specifications were developed. Based on them, the various units of measurement were completed successfully. The measurement process of scatterometer was briefly introduced in this work. according to different scattering mechanisms, features are divided into two categories: surface scattering and volume scattering, and analyzed these Scattering Characteristics and the reasons for these differences; combining with the corresponding earth substances scattering model, quantitative studying a function between the backscattering coefficient and surface parameters, getting different surface parameters of features by inversion, and analysis of a variety of influencing factors by comparing the measured datas.

The complex dielectric permittivity of pure ice is measured using the transmission/reflection method in the rectangular waveguide at frequencies between 2.6GHz and 3.9GHz and over the temperature range from -25 to -2.5°C, in order to extract the influence of temperature and frequency on the dielectric properties of ice quantitatively. The S band of microwave frequency is particularly investigated because of its importance and specialty. The experiments in this study show that the real part of the complex permittivity of pure ice is around 3.16, independent of frequency. The imaginary part changes over the frequency with the nonlinear function and the special frequency point 3.3GHz is found. Two polynomial functions of temperature are selected for the real part and the imaginary part, respectively. The real part increases firstly and then decreases with the increasing temperature, while the imaginary part just monotonously increases with the increasing temperature.