MRIFE: A Mask-Recovering and Interactive-Feature-Enhancing Semantic Segmentation Network for Relic Landslide Detection

Relic landslides, formed over a long period, possess the potential for reactivation, making them a hazardous geological phenomenon. While reliable relic landslide detection benefits the effective monitoring and prevention of landslide disaster, semantic segmentation using high-resolution remote sensing images (HRSIs) for relic landslides faces many challenges, including the object visual blur problem, due to the changes of appearance caused by prolonged natural evolution and human activities, and the small-sized dataset problem, due to difficulty in recognizing and labeling the samples. To address these challenges, a semantic segmentation model, termed mask-recovering and interactive-feature-enhancing (MRIFE), is proposed for more efficient feature extraction and separation. Specifically, to address the visual blur problem, a contrastive learning and mask reconstruction approach is designed under the guidance of remote sensing visual interpretation expert knowledge, which states the height variation at the landslide boundary contributing the most to landslide identification. This approach constructs local patches from the landslide boundary and background to perform supervised contrastive learning and applies mask reconstruction to local patches, guiding the model to focus on the landslide boundary and to extract the most contributive local salient features for reliable recognition. Meanwhile, to address the small-sized dataset problem, a self-distillation learning method is introduced, which uses a momentum encoder to update the teacher network with the average of the student network, suppressing overfitting caused by background interference. By constructing contrastive input pairs, the approach increases the diversity and combinations within the contrastive sample space and improves sample utilization. The proposed MRIFE is evaluated on a real relic landslide dataset, and experimental results show that it greatly improves the performance of relic landslide detection. For the semantic segmentation task, compared to the baseline, the precision increases from 0.4226 to 0.5347, the mean intersection over union (mIoU) increases from 0.6405 to 0.6680, the landslide IoU increases from 0.3381 to 0.3934, and the $F1$ -score increases from 0.5054 to 0.5646.