A Network for Landslide Detection Using Large-Area Remote Sensing Images with Multiple Spatial Resolutions

Abstract

Accurately detecting landslides over a large area with complex background objects is a challenging task. Research in the area suffers from three drawbacks in general. First, the models are mostly modified from typical networks, and are not designed specifically for landslide detection. Second, the images used to construct and evaluate models of landslide detection are limited to one spatial resolution, which struggles to meet the requirements of such relevant applications as emergency response. Third, assessments are primarily carried out by using the training data on different parts of the same study area. This makes it difficult to objectively evaluate the transferability of the model, because ground objects in the same area are distributed with similar spectral characteristics. To respond to the challenges above, this study proposes DeenNet, specifically designed for landslide detection. Different from the widely used encoder–decoder networks, DeenNet maintains multi-scale landslide features by decoding the input feature maps to a large scale before encoding a module. The decoding operation is conducted by deconvolution of the input feature maps, while encoding is conducted by convolution. Our model is trained on two earthquake-triggered landslide datasets, constructed using images with different spatial resolutions from different sensor platforms. Two other landslide datasets of different study areas with different spatial resolutions were used to evaluate the trained model. The experimental results demonstrated an at least 6.17% F1-measure improvement by DeenNet compared with three widely used typical encoder–decoder-based networks. The decoder–encoder network structure of DeenNet proves to be effective in maintaining landslide features, regardless of the size of the landslides in different evaluation images. It further validated the capacity of DeenNet in maintaining landslide features, which provides a strong applicability in the context of applications.