Landslides in mountainous regions exhibit widespread distribution and high recurrence, posing critical threats to human safety and socioeconomic assets. Interferometric Synthetic Aperture Radar (InSAR) has proven instrumental in landslide identification, continuous monitoring, and early-warning systems. Nevertheless, the inherent side-looking geometry of SAR systems restricts displacement measurements to the line-of-sight (LOS) direction, which may misrepresent true slope-parallel displacement and cause substantial inconsistencies between ascending/descending track-derived results and actual ground movement. To address this limitation, this study introduces the concept of LOS sensitivity (LS) and systematically quantifies geometric distortion effects on LOS measurements across diverse slope gradients and aspects. We propose an innovative LOS Sensitivity Correction (LSC) framework that integrates geometric distortion correction through joint optimization of topographic parameters and SAR interferometric processing configurations, enabling precise retrieval of slope-parallel displacement from single-track InSAR observations. Validation experiments utilizing Sentinel-1 ascending/descending datasets over the Ermulin and Huanglianping landslides demonstrate that while conventional D-InSAR results exhibit significant inter-track discrepancies (exceeding 40 mm in magnitude), LSC-processed displacements achieve remarkable consistency in deformation patterns (correlation coefficient >0.92) and spatial distribution. We further analyzed the influence of topographic parameters and interferometric processing configurations on LSC, revealing their effects on the correction results. The proposed methodology comprehensively resolves LOS sensitivity challenges for slopes with arbitrary geometries, advancing the fundamental understanding of terrain-dependent SAR detection limitations while transcending the simplistic formulaic application of conventional correction frameworks. This work establishes a paradigm for robust interpretation of slope-parallel kinematics from single-track InSAR products, with direct implications for landslide hazard assessment and mitigation strategies in complex terrains.

The maximum detectable deformation gradients (MDDG) for interferometric synthetic aperture radar (InSAR) technology is important for the selection of SAR images and processing algorithms to perform accurate slope displacement monitoring, which is strongly influenced by terrain factors in mountainous areas. In this paper, a functional model is proposed to determine the MDDG of InSAR with respect to arbitrary slope gradients/aspects and wavelengths. Based on this model, regional MDDG characteristics are explored and compared in Mao County, Sichuan Province, China. The MDDG distribution regarding on Sentinel-1, ALOS-2/PALSAR-2 and TerraSAR-X SAR satellite data using arbitrary slope gradient/aspect are derived. Furthermore, the MDDG from variable satellites for three different bands (X/C/L-band) are compared and the influence factors with respect to the wavelength and resolution on MDDG are discussed. The proposed model is helpful in selecting of SAR data or processing algorithms based on calculated MDDG, in the meanwhile, it has significant implications on the understanding and analyzing real slope displacement monitored by InSAR regarding on different SAR images in mountainous areas.

In the context of global warming, monitoring glacial lakes is of great significance for revealing climate change and mitigating glacier lake outburst floods (GLOFs). Multitemporal mapping of glacier lakes through optical satellite remote-sensing imageries can only provide us comparative analysis of glacier lake changes during long time intervals. In recent years, the rapid development of synthetic aperture radar (SAR) has made it possible to observe seasonal changes of glacial lakes. This letter proposed a polarization enhancement-based maximum interclass variance (PE-MIV) method to accurately and automatically mapping the seasonal cycles of glacial lakes based on multipolarization SAR data. Taking the Gongcuo and Langcuo glacial lakes in Laigu village, Tibetan Plateau, as examples, based on 113 Sentinel-1 SAR imageries covering 2017 to 2020 with a 12-day revisiting time, the seasonal boundary changes of both lakes were extracted automatically with a relative accuracy of 93.59%. The seasonal variations of glacial lake area are closely related to temperature and precipitation. This letter demonstrated that the satellite SAR (short revisiting time, multipolarization, etc.) is an effective tool to monitor areal changes of glacier lakes, which can be widely applied in detecting glacier lakes, GLOFs, and their response to climate change.

Although the widely used time-series InSAR technology makes up for the shortcomings of traditional geological investigation, such as small coverage and low efficiency, it cannot achieve rapid and dynamic detection of new potential landslide due to its long data processing time and insensitivity to short-term new displacement. In this paper, a rapid method for automatically detection new potential landslides in wide area is proposed. Phase-gradient processing is performed based on the DInSAR results to automatically detect the potential landslide, in which the influence from geometric distortion, water, noise from low coherence area, and other errors are analyzed and removed. This method was performed in the Maoergai Reservoir Area where many potential landslides newly emerged during the impoundment period as the great water level fluctuations. As a result, 7 potential landslides with continuous deformation and relatively large deformation were detected. The error source was analyzed and removed. In the validation, an overall accuracy of up to 81% was achieved by comparing the results with the manual detection. This method provides a new way for rapid and automatic detection of new displacements in wide-area, especially for the area (e.g. reservoir area) with dynamic and rapid detection needs.

Change detection (CD) with hyperspectral images (HSIs) can be effectively performed using deep learning networks (DLNs) by taking advantage of HSIs for their abundant spectral and spatial information and the excellent performance of DLN in machine learning. By modeling the temporal dependence of multiscale representative features, more discriminative information reflecting land use and land cover (LULC) changes can be obtained by suppressing less correlated information while improving the robustness of pseudo-changes caused by imaging noises. However, preserving time-dependent multiscale representative features while extracting spatial–spectral features based on conventional DLN is difficult, mainly due to the structural limitation of conventional DLN. A multipath convolutional long short-term memory (LSTM) multipath convolutional long short-term memory neural network (MP-ConvLSTM) taking advantage of LSTM and convolutional neural network (CNN) through the designed parallel architecture to learn multilevel temporal dependencies of bitemporal HSIs, therefore, was proposed for extracting multiscale temporal–spatial–spectral features by combining hidden states from different paths of ConvLSTM in the present study. In the proposed MP-ConvLSTM, the efficient channel attention (ECA) module was introduced to refine features of different paths, and Siamese CNN was adopted to reduce HSIs’ dimensionality and extract preliminary features to build up an end-to-end trainable model for CD with HSIs. The validity of the MP-ConvLSTM was evaluated using the binary and multiclass CD datasets. The CD accuracy of the proposed MP-ConvLSTM was visually and statistically evaluated by different criteria and compared with those derived from several state-of-the-art (SOTA) CD algorithms. The experiments demonstrated that our proposed model not only outperformed those SOTA CD models but also exhibited better tradeoff between complexity and accuracy in general.

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.