Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (V_slope). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (V_Slope) that varies between 0 and 364 mm/year. A total number of 234 landslides were cited from the literature and classified while 29 new potential landslides were detected and several pre-existing landslides were redefined by the InSAR approach, which was incorporated to generate an updated landslide susceptibility model with 86.6% of prediction precision in the area under curve method. As previous studies done by applying the InSAR technique incorporated a short span temporally and they missed some highly deforming zones like Budalas and Khanabad landslides, contain mean velocities > 50 mm/yr, which we studied individually in this work. In this study, a comprehensive application of the InSAR technique to assessing its performance in detecting and analysing landslides has been applied. The deformation velocity (V_slope) model shows high displacement in some regions, which needed to be further investigated by geoscientists, and the updated developed landslide inventory and susceptibility map can be used for land use planning and landslide mitigation strategies.

The speckle noise inherent in synthetic aperture radar (SAR) imaging has long posed a challenge for SAR data processing, significantly affecting image interpretation and recognition. Recently, deep learning-based SAR speckle removal algorithms have shown promising results. However, most existing algorithms rely on convolutional neural networks (CNN), which may struggle to effectively capture global image information and lead to texture loss. Besides, due to the different characteristics of optical images and synthetic aperture radar (SAR) images, the results of training with simulated SAR data may bring instability to the real-world SAR data denoising. To address these limitations, we propose an innovative approach that integrates swin transformer blocks into the prediction noise network of the denoising diffusion probabilistic model (DDPM). By harnessing DDPM’s robust generative capabilities and the Swin Transformer’s proficiency in extracting global features, our approach aims to suppress speckle while preserving image details and enhancing authenticity. Additionally, we employ a post-processing strategy known as pixel-shuffle down-sampling (PD) refinement to mitigate the adverse effects of training data and the training process, which rely on spatially uncorrelated noise, thereby improving its adaptability to real-world SAR image despeckling scenarios. We conducted experiments using both simulated SAR image datasets and real SAR image datasets, evaluating our algorithm from subjective and objective perspectives. The visual results demonstrate significant improvements in noise suppression and image detail restoration. The objective results demonstrate that our method obtains state-of-the-art performance, which outperforms the second-best method by an average peak signal-to-noise ratio (PSNR) of 0.93 dB and Structural Similarity Index (SSIM) of 0.03, affirming the effectiveness of our approach.

Accurate change detection continues to pose challenges due to the continuous renovation of old urban areas and the emergence of cloud cover in coastal areas. There have been numerous methods proposed to detect land-cover changes from optical images. However, there are still many flaws in many existing deep learning methods. In response to the problems of unpredictable change details and the lack of global semantic information in deep learning-based change detection models, a change detection model based on multi-scale and attention is proposed. Firstly, a multi-scale attention module is proposed to effectively obtain multi-scale semantic information to build an end-to-end dual multi-scale attention building change detection model. Secondly, an efficient double-threshold automatic data equalization rule is proposed to address the imbalance of data categories existing in the building change detection dataset, which effectively alleviates the severely skewed data distribution and facilitates the training and convergence of the model. The validation experiments are conducted on three open-source high-resolution building change detection datasets. The experimental results show that the proposed method in this paper can detect the location and area of the actual building changes more accurately and has better results in the detail detection part. This verifies the effectiveness and accuracy of the proposed method.

Phase unwrapping (PU) is a critical step in interferometric synthetic aperture radar (InSAR) processing chain, which can directly affect the quality of the final results. In order to improve the accuracy and robustness of PU algorithm, especially for the interferograms that have relatively long perpendicular baselines, in this article, we propose a new 3-D minimum cost flow (MCF) algorithm based on closure phase. Different from current 3-D PU algorithms that aim to solve the unwrapped phase gradients (UPGs) among different interferograms and usually follow a 1+2D mode, this new approach establishes a mathematical constraint within the closure phases and unwraps them jointly. This constraint is based on a prior knowledge that the sums of UPGs, where the UPGs are in the azimuth/range plane whereas belongs to different interferograms in the closure phase, can be obtained before PU. The main advantage of this new approach is that neither a temporal deformation model nor a preliminary atmospheric phase calibration is required before PU. Besides, this new approach combines the MCF model and phase consistency, showing advantages in reducing the chance of phase aliasing. We implement our new approach in both continuous and discrete interferograms and use real SAR data to validate the performance by comparing with the 2-D MCF algorithm in SNAPHU and the 3-D PU algorithm in StaMPS.

Rapid and accurate mapping of large-scale landslides is crucial for post-disaster prevention and risk analysis. This research introduces a new method named SPAUNet to address the challenges of noise and information redundancy when utilizing Synthetic Aperture Radar (SAR) amplitude data for landslide detection. This process is based on constructing a UNet model and incorporating a pixel attention mechanism. This study uses pre- and post-disaster SAR amplitude images as input data, generates seven pairs of dual-band combinations through registration, and inputs them into the model in sequence to automatically explore the best polarization amplitude combination, achieving rapid per-pixel mapping of large-scale landslides. Empirical analysis of landslide events in Milan and Papua New Guinea shows that SPAUNet outperforms the baseline models, improving the F1 score by 17% in Milan and 19% in Papua New Guinea. Moreover, this study emphasizes the importance of choosing the appropriate polarization combination for the region. The results indicate that SPAUNet, along with the appropriate polarization amplitude combination, improves the accuracy and reliability of landslide mapping, aiding disaster assessment and recovery work. This improvement holds significant implications for landslide disaster assessment and post-disaster recovery, providing a valuable direction for further enhancing landslide monitoring capabilities.

Conducting landslide recognition research holds notable practical significance for disaster management. In response to the challenges posed by noise, information redundancy, and geometric distortions in single-orbit SAR imagery during landslide recognition, this study proposes a dual-polarization SAR image landslide recognition approach that combines ascending and descending time-series information while considering polarization channel details to enhance the accuracy of landslide identification. The results demonstrate notable improvements in landslide recognition accuracy using the ascending and descending fusion strategy compared to single-orbit data, with F1 scores increasing by 5.19% and 8.82% in Hokkaido and Papua New Guinea, respectively. Additionally, utilizing time-series imagery in Group 2 as opposed to using only pre- and post-event images in Group 4 leads to F1 score improvements of 6.94% and 9.23% in Hokkaido and Papua New Guinea, respectively, confirming the effectiveness of time-series information in enhancing landslide recognition accuracy. Furthermore, employing dual-polarization strategies in Group 4 relative to single-polarization Groups 5 and 6 results in peak F1 score increases of 7.46% and 12.07% in Hokkaido and Papua New Guinea, respectively, demonstrating the feasibility of dual-polarization strategies. However, due to limitations in Sentinel-1 imagery resolution and terrain complexities, omissions and false alarms may arise near landslide edges. The improvements achieved in this study hold critical implications for landslide disaster assessment and provide valuable insights for further enhancing landslide recognition capabilities.