Change detection (CD) is the process of identifying changes in the category or attributes of ground objects by observing remote sensing images (RSI) taken at different times. In recent years, transformers have shown great potential in CD. However, current transformer-based CD networks have not fully exploited the capabilities of the transformer, especially when fusing features from bi-temporal and multi-stages. In this work, a pure transformer-based CD network (TFN) is built to fuse features better. Specifically, we build a Siamese CD network based on the transformer. Bi-temporal features are fused through a Shifted Window Fusion Model (SWFM) to address the misalignment between the features. In the decoding phase, a Multi-Scale Transformer Decoder (MSTD) is introduced to generate more complete change masks. The proposed method is validated on the WHU and SECOND datasets, demonstrating state-of-the-art performance (SOTA).

To overcome the effect of speckle noise on SAR image change detection, a superpixel segmentation algorithm based on speckle reducing anisotropic diffusion model is proposed and applied for object-oriented SAR image change detection. Based on the traditional modeling methods of the non-similarity between pixels and seed points by combining spectral distance and spatial distance in superpixel segmentation, the concept of diffusion flux is proposed to simulate the continuous and bounded evolution of the membership of pixels and seed points in the image plane lattice. Considering the effect of speckle noise and the demand of segmenting different shape surface features in complex scenes, the speckle reducing anisotropic diffusion is used to model the diffusion flux. After superpixel segmentation of dual-temporal remote sensing images, an overlay technology is adopted to obtain the finer results. Finally, the change detection result is generated based on the superpixelized difference image by the classical fuzzy clustering algorithm FCM. The experiments carried out on Sentinel-1 SAR images by comparing algorithms fully demonstrate the effectiveness of the proposed algorithm.

This study proposed a new application paradigm of time series SAR data for sugarcane mapping. First, the LOESS smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multitemporal SAR images with high importance values. At last, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate classification map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the "salt and pepper" spots and improving the completeness of the sugarcane plots. Especially, the unreasonable non-vegetation spots in the sugarcane map were completely eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation.

This study proposes a method for the three-dimensional (3D) inversion of Interferometric Synthetic Aperture Radar (InSAR) time series measurements, focusing on land subsidence in urban contexts characterized by slow, long-term, and small deformation magnitudes. Tailored for situations primarily dependent on single-track SAR satellite data, it integrates a physical constraint model between horizontal and vertical deformation gradients. Given the localized nature of urban deformations, this method avoids using a uniform subsidence model for an entire Region of Interest (ROI). Instead, it opts for a pixel-by-pixel estimation of constraint parameters, based on a detailed analysis of subsidence in affected areas. Experiments were conducted in representative scenarios, including the subsidence observed in ocean reclaimed zones and in residential areas impacted by tunneling activities for metro line construction. The results, validated through comparison with leveling measurements, suggest that the proposed method facilitates a precise 3D inversion, capturing the complex dynamics of urban surface deformation.

Cloud detection is a crucial step in the analysis and processing of optical remote sensing satellite imagery. Existing methods often have large parameter sizes, high computational complexity, and experience a rapid drop in model accuracy when transferred to different sensors. We propose a cloud detection method based on Efficient Transformer and Neural Style Transfer, a lightweight cloud detection network that can be used across different sensors without requiring additional labeled data. Our contribution focus on two main aspects: 1.We design a lightweight cloud detection model that reduces redundant parameters without compromising model accuracy; 2.We introduce a Neural Style Transfer module for cross-sensor cloud detection, aiming to align cloud features from different sensors with training images. Experiments on the 38-Cloud dataset demonstrate that our proposed method achieves state-of-the-art performance while maintaining a small parameter size and floating-point computation. In cross-sensor cloud detection experiments, the inclusion of the Neural Style Transfer module significantly enhances the model’s capability for cross-sensor cloud detection.

This study investigates the potential of using satellite-retrieved biophysical variables to address the scarcity of agricultural management data when modeling crop productivity across heterogeneous fields with a terrestrial biosphere model (TBM). A two-season field trial was conducted in Spain, providing various combinations of nitrogen (N) fertilization and irrigation levels. The crop responses to these management levels were found to be well represented by the Leaf Area Index (LAI) retrieved from the Sentinel-2 data. The satellite-retrieved LAI was then incorporated into a terrestrial biosphere model to estimate crop biomass. This satellite-derived model produced accurate biomass estimates with an overall R2of 0.52 and RMSE of 269.7 g m-2(42.6%), with no prior knowledge of management practices nor local calibration. This study confirms the capability of satellite remote sensing to capture crop responses to management practices and highlights its potential to optimize resource use efficiency in agricultural systems.

A feature set that can fully reflect information regarding the cumulative dryness state (CDS) of forest fuels is crucial in forest fire risk modeling. Due to the uneven spatial and temporal distribution of rainfall, the CDS information often exhibits significant spatial heterogeneity. Current feature extraction methods for environmental factors based on fixed time windows struggle to capture this spatial heterogeneous information accurately. This paper proposes an adaptive time window-based method for extracting forest environmental factors features. By using precipitation as a constraint, this method adaptively constructs dynamic time windows for each pixel, thereby obtaining finer CDS information. The random forest (RF) and support vector machine (SVM) algorithms were used to construct the fire risk models, and both showed improvements in overall accuracy, indicating the effectiveness of the proposed method. The improvement performance of the RF model was better than that of the SVM model, and the overall accuracy can be improved by 5% to 8% under appropriate precipitation constraint settings.

Stripe noise still affects full-spectrum airborne hyperspectral imager (FAHI) images after laboratory radiometric calibration, which seriously affects the subsequent applications of the imager. Therefore, two state-of-the-art methods, median linear correction (MLC) and Fourier transform filtering (FTF), were proposed to restore FAHI images, and the residual stripes were removed in most cases. However, these methods have their own limitations. For instance, the restored image has a slight “shadow” in cases where the high-response digital numbers (DNs) of the detector are aligned with the flight direction. This letter proposes a new method based on improved statistics to restore FAHI images. In this method, the hyperspectral image data from the adjacent flight paths is used to obtain the uniform response DNs for nearly identical low and high irradiances. Subsequently, a statistics-based MLC method is used to eliminate the stripe noise. To quantitatively evaluate the restoration results, we compared results with MLC and FTF methods. The change in mean value and mean relative deviation of the proposed method for the high-response DNs area of the image is 0.25% and 0.97%, respectively, better than that of the other two methods. The experimental results demonstrate that the proposed method is effective for removing stripe noise and preserving accurate image information of push-broom hyperspectral imagery.

Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications.

Multioutput Gaussian processes (MOGPs) are an extension of Gaussian processes (GPs) for predicting multiple output variables (also called channels/tasks) simultaneously. In this article, we use the convolution theorem to design a new kernel for MOGPs by modeling cross-channel dependencies through cross convolution of time-and phase-delayed components in the spectral domain. The resulting kernel is called multioutput convolution spectral mixture (MOCSM) kernel. The results of extensive experiments on synthetic and real-life data sets demonstrate the advantages of the proposed kernel and its state-of-the-art performance. MOCSM enjoys the desirable property to reduce to the well-known spectral mixture (SM) kernel when a single channel is considered. A comparison with the recently introduced multioutput SM kernel reveals that this is not the case for the latter kernel, which contains quadratic terms that generate undesirable scale effects when the spectral densities of different channels are either very close or very far from each other in the frequency domain.

Multitask Gaussian processes (MTGPs) are a powerful approach for modeling dependencies between multiple related tasks or functions for joint regression. Current kernels for MTGPs cannot fully model nonlinear task correlations and other types of dependencies. In this article, we address this limitation. We focus on spectral mixture (SM) kernels and propose an enhancement of this type of kernels, called multitask generalized convolution SM (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and dependence between components, including time and phase delay dependence. Each task in MT-GCSM has its GCSM kernel with its number of convolution structures, and dependencies between all components from different tasks are considered. Another constraint of current kernels for MTGPs is that components from different tasks are aligned. Here, we lift this constraint by using inner and outer full cross convolution between a base component and the reversed complex conjugate of another base component. Extensive experiments on two synthetic and three real-life data sets illustrate the difference between MT-GCSM and previous SM kernels as well as the practical effectiveness of MT-GCSM.

Mangrove species mapping is imperative to understand their vegetation dynamics better, such as succession, deforestation, stand density and health conditions, as well as further understanding the ecological services they provide. With the availability of high spectral and high spatial resolution remote sensing images, it is feasible to digitally mapping mangrove forests to the species level [1] .

Abstract Gaussian processes (GPs) are an elegant Bayesian approach to model an unknown function. The choice of the kernel characterizes one’s assumption on how the unknown function autocovaries. It is a core aspect of a GP design, since the posterior distribution can significantly vary for different kernels. The spectral mixture (SM) kernel is derived by modelling a spectral density - the Fourier transform of a kernel - with a linear mixture of Gaussian components. As such, the SM kernel cannot model dependencies between components. In this paper we use cross convolution to model dependencies between components and derive a new kernel called Generalized Convolution Spectral Mixture (GCSM). Experimental analysis of GCSM on synthetic and real-life datasets indicates the benefit of modeling dependencies between components for reducing uncertainty and for improving performance in extrapolation tasks.

In this paper, based on Lee generalized decomposition model, an adaptive two-component decomposition model is proposed. The PolSAR coherency matrix is represented as the sum of two scattering mechanisms: coherent ground scattering and incoherent volume scattering. The proposed model is under three assumptions: 1) the surface and double scattering are coherent, 2) the surface and double scattering are integrated as the ground scattering, and 3) the average polarimetric orientation angle (POA) of the volume (or Bragg) scattering is zero. The proposed model has three advantages: 1) it can successfully avoid the negative power problem, 2) it is considered without the assumption of reflection symmetry, and 3) the dominant scattering mechanism criterion is not needed in the process of model inversion. The Polarimetric ESAR L-band data of Oberpfaffenhofen were used to show the efficiency of the proposed decomposition model.

The deep learning has been widely used in scene recognition of remote sensing images. However, the accuracy of deep learning relays on the size of training dataset to the utmost. The remote sensing images have various spatial scales and semantics, which are not fully considered in the existing datasets. In this paper, a benchmark remote sensing dataset named as Cross-Scale Remote Sensing dataset of Shenzhen Institutes of Advanced Technology (CSRS-SIAT) is proposed, which has about 100 classes according to the land cover and land use field, and due to the cross-scale characteristics of remote sensing images, the experiments using traditional and state-of-the-art deep learning algorithms shows that there still need more efforts to achieve better results.

Fitting polarimetric synthetic aperture radar (PolSAR) data with adaptive scattering models is a promising way to mitigate the deficiencies of the model-based decomposition. Recently, Lee et al. have proposed a generalized decomposition model with several adaptive parameters, whereas the generalized model introduces too much freedom to be solved. In this paper, based on the Lee generalized decomposition model, an adaptive two-component decomposition model is proposed. The PolSAR coherency matrix is represented as the sum of two scattering mechanisms: coherent ground scattering and incoherent volume scattering. The proposed model is under three assumptions: 1) Surface and double scattering are coherent; 2) surface and double scattering are integrated as the ground scattering; and 3) the average polarimetric orientation angle (POA) of the volume (or Bragg) scattering is zero. As the proposed model is very difficult to solve directly, we adopted the exhaustion technique to find the best fit parameter set. The proposed model has three advantages: 1) It can successfully avoid the negative power problem; 2) it is considered without the assumption of reflection symmetry; and 3) the dominant scattering mechanism criterion is not needed in the process of model inversion. However, the proposed model has two disadvantages: 1) the attribution of the volume model becomes ambiguous; and 2) the assumption that sets the POA of the Bragg scattering component to zero is inconsistent with the actual scattering mechanism when there is a slope in the rough surface. The polarimetric AIRSAR L-band data of San Francisco and ESAR L-band data of Oberpfaffenhofen were used to show the efficiency of the proposed decomposition model. Statistical properties of typical areas showed that, except the sea surface and the urban area with building orientation angle about 45°, the proposed model fits the PolSAR data very well.

The assumption of reflection symmetry is one of the major deficiencies for the model based decompositions. The introduction of helix scattering component can mitigate the impact of this assumption limitedly, while it generates some new negative power problems simultaneously. In this paper, we expand the techniques of symmetric scatterer transformation in the Cameron and Huynen decompositions to the multi-look coherency matrix. Two new models of nonlinear programming problem are proposed to mitigate the reflection symmetry assumption and negative power problems for the model based decomposition. Experimental results in the L-band AIRSAR San Francisco data show that the two new models can reduce the correlations between co-polarized and cross-polarized channels to an insignificant level.

Building orientations with respect to the radar look direction is a critical influence on the interpretation of PolSAR data in urban areas. In this paper, we try to analyze thoroughly the problems of polarimetric scattering mechanism in urban areas induced by building orientation. For multi-look PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, it can be inferred that with the increase of the building orientation, the POA estimation circular-polarization method and the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the POA compensation processing is helpful to reduce the impacts of the building orientation, but when the building orientation is increased to some degree, it also become invalid. Three L-band datasets of San Francisco acquired by AIRSAR are used to verify the inferences.

The assumption of reflection symmetry is one of the major deficiencies for model-based decompositions. The introduction of helix scattering components can mitigate some of the impact of this assumption while simultaneously generating some new negative power problems. The helix mechanism has been developed in the Krogager coherent target decomposition where there is no negative power problem. Why do these problems happen in model-based decompositions? In this paper, we review the Krogager decomposition based on the coherency matrix form of the unified CTD model and come to the conclusion that the imaginary part of T23is an overestimate of the helix contribution, which is the main cause of the problem. Furthermore, we review the techniques of symmetric scatterer transformation in the Cameron and Huynen decompositions and expand them to the multilook coherency matrix. Two new models of the nonlinear programming problem are proposed to get the transformed coherency matrix: 1) by subtracting the asymmetric component; and 2) by the procedure of con-diagonalization. A new parameter is established to measure the degree of reflection asymmetry of the coherency matrices. Experimental results in the L-band Airborne SAR (AIRSAR) San Francisco data show that the two new models can reduce the correlations between copolarized and cross-polarized channels significantly. In this case, the assumption of reflection symmetry is reasonable for the transformed coherency matrices, and negative power problems related to helix scattering no longer exist in the model-based decompositions; hence, the helix scattering component is not needed. Lastly, the Freeman and Durden decomposition has been applied to the transformed coherency matrices. Experimental results showed that the decomposition results can reflect the predominant characteristics of the ground objects.

Building orientation with respect to the radar look direction has a critical influence on the interpretation of multilook polarimetric synthetic aperture radar (PolSAR) data in urban areas. In this paper, its impacts on polarimetric orientation angle (POA) estimation and model-based decomposition are discussed. The discussion begins with the analysis of the general double-bounce scattering model, of which the characteristics are dependent on the electromagnetic and geometric parameters of the related dihedral structure. Then, for multilook PolSAR data, the polarimetric scattering mechanism in urban areas is modeled by two double-bounce scatterings from two orthogonal dihedral structures. From the model, the impacts of the building orientation on POA estimation can be revealed. With the increase of the building orientation, the POA difference between the two dihedral structures increases gradually, and the feasibility to estimate the building orientation via the estimated POA is reduced dramatically. Upon further analysis, we illustrate the impacts on the model-based decomposition. With the increase of the building orientation, the dominant scattering mechanism labeling technique based on the model-based decompositions will gradually become invalid. Moreover, the processing of POA compensation, which is helpful in reducing the impacts of the building orientation, also becomes invalid when the building orientation increases to a certain value. At last, three L-band data sets of San Francisco acquired by AIRSAR are used to verify the inferences. The experimental results show that, for L-band PolSAR data in urban areas, when the radar look angle is around 45, the threshold of building orientation for the validity of dominant scattering mechanism labeling is about ±3, and for the POA compensation, the threshold is about ±12.

This letter presents preliminary results of sugarcane mapping in its tillering period using quad-polarization data. Three TerraSAR-X images were collected in early, middle, and late tillering periods in the Leizhou Peninsula, South China. Polarization features based on Cloude-Pottier decomposition, such as scattering angle and polarization entropy, were used to analyze sugarcane scattering mechanism temporal behavior and the differences between sugarcane and other typical land-cover types. Results show good separability of sugarcane from other land-cover types. At last, based on common features of sugarcane in tillering period, a sugarcane mapping method was proposed with certain high accuracy and good stability. The results suggest that the tillering period, from early April to late May, is a suitable growth period for sugarcane mapping, and polarization features appear promising for crop mapping and monitoring.

A new integrated SAR signal processor, geo-coding and interferometric processing commercial software (GeoWatch software) has been developed. It supports high speed and large data processing based on ERS-1&2, ENVISAT, ALOS PALSAR, JERS-1, RADARSAT-1&2, TerraSAT and CSK SAR data, with user friendly GUI and parallel processing on the latest 64 bit Windows and UNIX/Linux systems and both personal computers and powerful multicore CPU+GPU cluster platforms. It has been applied to the DSM estimation in a mountain area and subsidence estimation at an airport using its application-oriented step-by-step pipeline processing, and the results showed the following key features: 1. Deformation monitoring coverage expansion from coherent points to distributed scatters; 2. Deformation monitoring period reduction and accuracy improving by multi-track SAR interferometric processing; 3. Truly ortho-rectified interferogram, image, digital surface model (DSM) and deformation map production.

The coupling relationship between urban vegetation and land surface temperature (LST) has been of great interest to a variety of environmental studies. This paper retrieves the urban vegetation and LST utilizing Terra ASTER imagery in the year 2007, and analyzes their coupling relationships accordingly, so as to provide the basis for decision making of ecological planning and environment protection. It turns out that NDVI, urban vegetation abundance (UVA) and urban forest abundance (UFA) are all in negative correlation with land surface temperature (LST), so that Urban vegetation and urban forest are both capable in decreasing LST. The influence of urban vegetation and urban forest varies with pixel aggregation, and peaks around 90 m ~ 120 m resolution. At the end of this paper, some future efforts on the analysis are pointed out.

Land surface temperature (LST) is of great interest to a variety of environmental studies. In this paper, Beijing is chosen as study area, and a time series of Landsat TM/ETM+ and ASTER images are utilized to learn the distribution and change of LST within this city. Generalized single-channel method and split-window algorithm are applied respectively in the retrieval of LST from Landsat TM/ETM+ images and ASTER images. The LST data are converted to histogram equalized maps and then synthetically compared and analyzed. Spatial distribution of LST in the summer of each year is concluded, as well as the temporal diversion. It is learned that the Heat Island Effect has been mitigated since the year 1999. In the final part, the achievement and disadvantages of this analysis are concluded, and the future efforts are pointed out as well.

Most paddy rice in southern China grows in warm, humid and rainy areas where it is hard to acquire optical remote sensing data. In this study, a semi-empirical backscattering model was proposed to estimate leaf area index (LAI) of rice in the area using ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization data. Ground measurements of LAI, water content and height of rice in the test site were collected and the model fitted at the same time as the acquisition of ASAR data. LAI estimated from the model was compared with ground measurements to evaluate the accuracy of the model. The results showed that the model provides a promising alternative to optical remote sensing data for predicting LAI of rice in southern China.

Urban forest is of great interest to a variety of scientific and urban planning applications. This paper presents a strategy for calculating tree canopy density in urban areas using Landsat ETM+ imagery and calculating its ecological value. The strategy consists of two key steps: one is to extract urban tree canopy area from remote sensing images; the other is to calculate the ecological value of urban forest. The extraction of tree canopy area from remote sensing imagery is carried out using classification models, which are based on empirical relationships between forest coverage and the spectrum on Landsat imagery, and are generated using regression tree techniques. The calculation about urban forest is carried out introducing CITYgreen model. In the analysis, the zone inside the 4th Ring Road in Beijing is chosen as study area; and a Landsat ETM+ image taken in 2001 is used applying this strategy to analyze the urban forest. In the following part, the results of the analysis are presented. At the end, the accuracy of tree canopy density is reviewed, and some possible reasons for the error are discussed, as well as the advantages and disadvantages of this strategy.

Five spacecrafts named in the Shenzhou series (SZ in abbreviation) have been launched during 1999 to 2003 in China. Earth observation is a major scientific mission of the SZ spacecrafts. SZ-3 carried a 34-band medium resolution spectrometer, and SZ-4 was equipped with multimode microwave sensors composing a microwave scatterometer, radiometer, and radar altimeter. Our research group made "spacecraft-aircraft-ground" synchronous measurements. This paper presents the review of the mission of SZ spacecrafts, and gives the results of data processing and environment and geoscience applications.

CMODIS installed in SZ-3 spacecraft is the first Chinese medium resolution spectrometer with 34 channels in the range from the visible to the infrared. Unfortunately, sharp and repetitive strips over the image exist in many channels of CMODIS can distractingly and obstructively affects the interpretation and application of CMODIS data. This paper: 1) discusses the causes of the striping; 2) presents a new frequency domain finite impulse response filter (FIR) for removing the striping in the data with minimum distortion into the filtered data; 3) quantitatively compares the results obtained with the new filtering method with those produced by traditional destriping methods (e.g., low pass filtering, moment matching) on the CMODIS georectified and no-georectified data of inhomogeneous targets. Results show that the finite impulse response filter, implemented in frequency domain, is superior to other methods evaluated in destriping and preservation of image information. The importance of proper destriping for classification is demonstrated. The finite impulse response filter is also applicable in striping removal of other multisensor remote Sensing data.

Sharp and repetitive stripes exist in many bands of Chinese Moderate Imaging Spectroradiometer (CMODIS) images because of gain and offset variations between neighboring forward and reverse scans of 22 detectors, which can distractingly and obstructively affect the interpretation and application of CMODIS data. In this letter, we suggest a method for destriping based on a finite-impulse response (FIR) filter in frequency domain and present the results obtained with the method on the experimental CMODIS images of heterogeneous targets. We also compare the method with some traditional destriping methods (e.g. lowpass filtering, moment matching) and display the result of comparison using one of the images. The destriping effectiveness is evaluated by means of appropriate indexes of quality in this letter.