Multilevel Adaptive-Scale Context Aggregating Network for Semantic Segmentation in High-Resolution Remote Sensing Images

High-resolution remote sensing ((HRS)-S-2) images contain complex land objects of difference sizes, and it is important for semantic segmentation of the (HRS)-S-2 images to extract multiscale information. In this letter, we introduce a novel multilevel adaptive-scale context aggregating network (MACANet) for semantic segmentation of the (HRS)-S-2 images, which mainly consists of two parts--adaptive-scale context extraction block (AS-CEB) and sequential aggregation block (SAB). In particular, the AS-CEB introduces an inflexible strategy to obtain the features with appropriate scale information based on different asymmetric convolutions and the gated mechanism. Meanwhile, the SAB progressively aggregates multilevel adaptive-scale features, which are used to relieve the semantic gap between different-level features and generate precise score maps. Experimental results on representative (HRS)-S-2 datasets show the advantages of our method. The code is available at https://github.com/RSIP-NUDT/MACANet.