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.

Image feature representation is a key factor influencing the accuracy of clustering. Traditional point-based feature spaces represent spectral features of an image independently and introduce spatial relationships of pixels in the image domain to enhance the contextual information expression ability. Mapping-based feature spaces aim to preserve the structure information, but the complex computation and the unexplainability of image features have a great impact on their applications. To this end, we propose an explicit feature space called Riemannian manifold feature space (RMFS) to present the contextual information in a unified way. First, the Gaussian probability distribution function (pdf) is introduced to characterize the features of a pixel in its neighborhood system in the image domain. Then, the feature-related pdfs are mapped to a Riemannian manifold, which constructs the proposed RMFS. In RMFS, a point can express the complex contextual information of corresponding pixel in the image domain, and pixels representing the same object are linearly distributed. This gives us a chance to convert nonlinear image segmentation problems to linear computation. To verify the superiority of the expression ability of the proposed RMFS, a linear clustering algorithm and a fuzzy linear clustering algorithm are proposed. Experimental results show that the proposed RMFS-based algorithms outperform their counterparts in the spectral feature space and the RMFS-based ones without the linear distribution characteristics. This indicates that the RMFS can better express features of an image than spectral feature space, and the expressed features can be easily used to construct linear segmentation models.

Synthetic aperture radar (SAR) image segmentation is a challenge due to its inherent speckle. Gamma distribution is believed to be an appropriate statistical model to describe the characteristics of speckle in SAR images. In this letter, a fuzzy clustering algorithm based on gamma distribution for SAR image segmentation is proposed, in which the Stirling equation is used to approach the gamma function in the dominator of gamma distribution under the assumption of mean field theory to make the shape parameter$\alpha $derivable. Then, the value range of the estimated$\alpha $is demonstrated to meet the requirement of gamma distribution by Jensen’s inequality. Experimental results show that the proposed method gives promising results in SAR image (including large area SAR image) segmentation and effectively suppresses the influence of speckle.