DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai–Tibet Plateau
نویسندگان
چکیده
At present, Synthetic Aperture Radar Interferometry (InSAR) has been an important technique for active landslides recognition in the geological survey field. However, traditional interpretation method through human–computer interaction highly relies on expert experience, which is time-consuming and subjective. To solve problem, this study designed end-to-end semantic segmentation network, called deep residual shrinkage U-Net (DRs-UNet), to automatically extract potential InSAR imagery. The proposed model was inspired by structure of adopted a building unit (RSBU) as feature extraction block its encoder part. three main advantages: (1) RSBU part incorporated with soft thresholding can reduce influence noise from images. (2) connection makes training network easier accelerates convergency process. (3) fusion corresponding layers between decoder effectively improves classification accuracy. Two widely used networks, SegNet, were trained under same experiment environment compare method. results test set show that our achieved best performance; specifically, F1 score 1.48% 4.1% higher than indicates better balance precision recall. Additionally, IoU over 90%. Furthermore, we applied area located Zhongxinrong County along Jinsha River where are evolved. quantitative evaluation prove effective automatic landslide hazards
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14081848