Comparison of Pixel-Level and Feature-Level Image Fusion Networks for Slow-Moving Landslide Detection

Slow-moving landslide detection is of vital importance in preventing and mitigating geohazards. Extracting abstract features from remote sensing images is crucial for achieving high-precision detection of slow-moving landslides. This study utilizes both activity features and terrain structure features for geohazard detection. We propose a pixel-level and a feature-level image fusion network, and investigate the multi-level fusion cooperative mechanism. We evaluate the performance of the two-level fusion and single-modal data base on the test data. The experimental results demonstrate that fusion of the activity characteristics and topographic characteristics can enhance the accuracy of identifying slow-moving landslides. Feature-level fusion outperforms pixel-level fusion for slow-moving landslides identification.