Semantic segmentation of remote sensing images plays a critical role in areas such as urban change detection, environmental protection, and geohazard identification. Convolutional Neural Networks (CNNs) have been excessively employed for semantic segmentation over the past few years; however, a limitation of the CNN is that there exists a challenge in extracting the global context of remote sensing images, which is vital for semantic segmentation, due to the locality of the convolution operation. It is informed that the recently developed Transformer is equipped with powerful global modeling capabilities. A network called TCNet is proposed in this article, and a parallel-in-branch architecture of the Transformer and the CNN is adopted in the TCNet. As such, the TCNet takes advantage of both Transformer and CNN, and both global context and low-level spatial details could be captured in a much shallower manner. In addition, a novel fusion technique called Interactive Self-attention is advanced to fuse the multilevel features extracted from both branches. To bridge the semantic gap between regions, a skip connection module called Windowed Self-attention Gating is further developed and added to the progressive upsampling network. Experiments on three public datasets (i.e., Bijie Landslide Dataset, WHU Building Dataset, and Massachusetts Buildings Dataset) depict that TCNet yields superior performance over state-of-the-art models. The IoU values obtained by TCNet for these three datasets are 75.34% (ranked first among 10 models compared), 91.16% (ranked first among 13 models compared), and 76.21% (ranked first among 13 models compared), respectively.

The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.