A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection
This work addresses landslide risk warning for disaster management by enhancing detection accuracy, though it appears incremental as it builds on existing segmentation models with specific data fusion and contrastive learning techniques.
The paper tackled the problem of detecting relic landslides from remote sensing images by proposing a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net), which improved mIoU from 0.620 to 0.651, Landslide IoU from 0.334 to 0.394, and F1-score from 0.501 to 0.565 on the Loess Plateau dataset.
As a natural disaster, landslide often brings tremendous losses to human lives, so it urgently demands reliable detection of landslide risks. When detecting relic landslides that present important information for landslide risk warning, problems such as visual blur and small-sized dataset cause great challenges when using remote sensing images. To extract accurate semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from boundaries of landslides through HPCL and fuses heterogeneous information in the semantic space from high-resolution remote sensing images and digital elevation model data. For full utilization of precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method is developed, which includes the construction of global queues that store hyper-pixel-wise samples and the updating scheme of a momentum encoder, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on the Loess Plateau relic landslide dataset and experimental results verify that the proposed HPCL-Net greatly outperforms existing models, where the mIoU is increased from 0.620 to 0.651, the Landslide IoU is improved from 0.334 to 0.394 and the F1score is enhanced from 0.501 to 0.565.