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A massive landslide often causes long-lasting instability dynamics that need to be analyzed in detail for risk management and mitigation. Multiple satellite remote sensing observations, in-situ measurements, and geophysical approaches have been jointly implemented to monitor and interpret the life cycle of landslides and their failure mechanisms from various perspectives. In this work, we propose a framework where satellite optical and synthetic aperture radar (SAR) remote sensing techniques are combined with feature extractions using independent component analysis (ICA) and a mathematical relaxation model to assess the complete four-dimensional (4D) spatiotemporal patterns of post-failure slope evolution. The large, deep-seated Aniangzhai landslide in Southwest China that occurred on 17 June 2020 is comprehensively analyzed and characterized for its post-failure mechanism. Time series of Planet high-resolution optical images are first explored to derive the large horizontal motions for the first six months after the failure. Spatiotemporal dynamics of line-of-sight (LOS) displacement in the landslide body are then derived between November 2020 and February 2022 by combining 40 TerraSAR-X (TSX) High-resolution Spotlight (HS) images and 76 medium-resolution Sentinel-1 (S1) SAR datasets using Multi-temporal InSAR (MTI) method. The InSAR-derived results are subsequently analyzed with ICA to find common deformation components of points between optical and MTI results, indicating the same temporal evolution in the deformation pattern. Finally, the complete 4D deformation field for the whole post-failure period is modeled using a decaying exponential model representing stress relaxation after the failure by integrating multiple remote sensing datasets. Cross-correlation analysis of Planet imagery shows a decaying exponential pattern of post-failure displacements with an approximately 94% reduction in the deformation rate after six months with respect to the co-failure event. MTI analysis suggests a maximum LOS displacement rate of approximately 30 cm/year over the main failure body from November 2020 to February 2022; while the high-resolution TSX datasets show irreplaceable advantages in choosing the number of measurement points in MTI analysis with the number of measurement points being five times larger than those obtained by S1 datasets. The ICA analysis reveals three main types of kinematic patterns in the temporal evolution of post-failure deformation in MTI results, the dominant one being an exponential declining pattern similar to the results from Planet observations. Integrated 4D deformation modeling suggests that the most significant post-failure displacement mainly occurred toward the west, amounting to 28 m during the entire post-failure acquisitions from June 2020 until February 2022. Additionally, maximum displacements of 17 m and 19 m occurred in this period toward the north and downward, respectively. [Display omitted] • Monitor Aniangzhai landslide integrating multi-sensor remote sensing data • Derive 4D post-failure slope instability dynamics • Implement ICA to analyze spatiotemporal characteristics of slope deformation • Propose a relaxation model to predict post-failure deformation [ABSTRACT FROM AUTHOR]

This paper addresses the unsupervised classification problem for multilook polarimetric synthetic aperture radar (PolSAR) images via proposing a new spatially variant G p 0 mixture model based on the extended variational inference (SV G p 0 MM-EVI). Specifically, an SV G p 0 MM is constructed by associating data points with their own mixing coefficient vectors rather than sharing the same vector, which provides the additional flexibility to incorporate the spatial context and further effectively deal with the more heterogeneous areas than G p 0 MM. Then, an extended variational inference (EVI) algorithm is derived to facilitate the assignment of the conjugate prior distributions and accurately estimate the underlying parameters, in which two "help" functions are specially designed to solve the intractable variational lower bound. Meanwhile, dual spatial constraints based on the Bartlett distance and the mean template are also explored on polarimetric matrix and the hyperparameter in the posteriori distribution of mixing coefficients, respectively, thus adequately capturing the local correlation from the polarimetric matrix and the geometry perspectives. Furthermore, the learning algorithm with all the closed-form updates is developed and the cluster number could be automatically determined. Five PolSAR data sets (including two labeled data sets), which are obtained by different sensors, are used to verify the effectiveness of the proposed model. Experimental results illustrate that the proposed SV G p 0 MM-EVI is beneficial to classification task of PolSAR images, and achieves higher accuracy (e.g., 95.65% and 97.41% OA values) compared with some widely-used methods (i.e., H/ α -Wishart, Chernoff–Wishart, G p 0 MM, SVWMM and CK-HDRF methods). [ABSTRACT FROM AUTHOR]

Automatic and high-precision extraction of buildings from remote sensing images has a wide range of application and importance. Optical and synthetic aperture radar (SAR) images are typical types of multimodal remote sensing data with different imaging methods. To bridge the huge gap between them and achieve high-precision joint semantic segmentation, this study proposes a progressive fusion learning framework. The framework explicitly extracts the shared features (that is, modal invariants) of multimodal images as the information medium and realizes information fusion through multistage learning. Based on this framework, we design a network called the multistage multimodal fusion network (MMFNet), which uses phase as a modal invariant to joint optical and SAR images to achieve high-precision building extraction. We conducted experiments with the Multi-Sensor All-Weather Mapping aerial dataset and the WHU-OPT-SAR_WuHan satellite dataset. This study shows MMFNet has a significant extraction effect and yields more optimized extraction of the edge details of buildings, which is improved by 0.2% to 9.5% compared to other multimodal joint segmentation methods. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is playing an important role in maritime domain awareness. As a fundamental ocean mission, SAR ship detection can offer high-quality services for marine surveillance. It receives widespread concern in the SAR remote sensing community. Recently, deep learning (DL) has greatly improved SAR ship detection accuracy. However, there are still some unperceived imbalance problems that seriously hinder further accuracy improvements among current DL-based SAR ship detectors. Therefore, we propose a novel concept of balance learning (BL) for high-quality SAR ship detection. We first point out the four unperceived imbalance problems, i.e., image sample scene imbalance, positive negative sample imbalance, ship scale feature imbalance, and classification regression task imbalance. Then, we offer some profound insights into these imbalances. Immediately, we propose four effective solutions to handle the above four imbalances correspondingly, i.e., balance scene learning mechanism (BSLM), balance interval sampling mechanism (BISM), balance feature pyramid network (BFPN), and balance classification regression network (BCRN). Finally, combined with the four solutions, a novel balance learning network (BL-Net) is proposed. Ablation studies can confirm each solution's effectiveness. Experimental results on five open datasets (SSDD, Gaofen-SSDD, Sentinel-SSDD, SAR-Ship-Dataset, and HRSID) reveal BL-Net's state-of-the-art SAR ship detection performance compared to the other 30 DL-based SAR ship detectors. Specifically, in contrast to the current most competitive method, the accuracy increase of BL-Net is 2.98% on SSDD, 1.97% on Gaofen-SSDD, 1.49% on Sentinel-SSDD, 0.55% on SAR-Ship-Dataset, and 4.95% on HRSID. Last but not least, the satisfactory ship detection results on another two large-scene Sentinal-1 SAR images confirm BL-Net's strong migration capability. This indicates BL-Net's potential value in marine surveillance. [ABSTRACT FROM AUTHOR]