Semantic Segmentation for Remote Sensing Images Based on Adaptive Feature Selection Network

Semantic segmentation plays a vital role in the segmentation of remote sensing field for its wide range of applications. The major current method for segmentation of remotely sensed imagery is using multiple scales strategy to improve the performance of segmentation networks. However, the ground object with uncertain scale in high-resolution aerial imagery is difficult to be segmented with conventional models. To address this problem, an adaptive feature selection module is designed, in which attention module learns weight contributions of each feature blocks in different scales. We employ the pyramid scene parsing network (PSPNet), DeepLabV3, and U-Net with the proposed module to conduct experiments on two benchmarks (the Vaihingen set and the WHU Building data set). The experimental results and comprehensive analysis validate the efficiency and practicability of the proposed method in semantic segmentation of remote sensing images.