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.

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.

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] .

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.

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 method is proposed to get the segmentation threshold and detect the dark spot in oil-spill images. The method is inspired from the K -distribution model of sea background, which is widely accepted to describe the ocean clutter. By comparing the histograms of oil-spill region and the sea background, it is found that the oil slicks break the K -distribution model, but there is still some information unchanged-the relative probability ratios among the pixel values in 95%~99% CDF extent, which is used to deduce the original κ -distribution model. Finally, the intersection of the original histogram of the oil spill image and the deduced sea background PDF is selected to be the threshold. Experiment in RADARSAT-2 image shows the effectiveness of the method.