MSCANet: multiscale context information aggregation network for Tibetan Plateau lake extraction from remote sensing images

Abstract

Qinghai-Tibet Plateau lakes are important carriers of water resources in the ‘Asian’s Water Tower’, and it is of great significance to grasp the spatial distribution of plateau lakes for the climate, ecological environment, and regional water cycle. However, the differences in spatial-spectral characteristics of various types of plateau lakes, and the complex background information of plateau both influence the extraction effect of lakes. Therefore, it is a great challenge to completely and effectively extract plateau lakes. In this study, we proposed a multiscale contextual information aggregation network, termed MSCANet, to automatically extract Plateau lake regions. It consists of three main components: a multiscale lake feature encoder, a feature decoder, and a Multicore Pyramid Pooling Module (MPPM). The multiscale lake feature encoder suppressed noise interference to capture multiscale spatial-spectral information from heterogeneous scenes. The MPPM module aggregated the contextual information of various lakes globally. We applied the MSCANet to the lake extraction of the Qinghai-Tibet Plateau based on Google data; additionally, comparative experiments showed that the MSCANet proposed had obvious improvement in lake detection accuracy and morphological integrity. Finally, we transferred the pre-trained optimal model to the Landsat-8 and Sentinel-2A dataset to verify the generalization of the MSCANet.

Keywords

• remote sensing imagery
• the qinghai-tibet plateau lake extraction
• deep learning
• multiscale feature
• context information aggregation