Semantic Segmentation With Attention Mechanism for Remote Sensing Images

Abstract

Semantic segmentation for high-resolution remote sensing images is one of the most significant tasks in the field of remote sensing applications. Remote sensing images contain substantial detailed information of ground objects, such as shape, location, and texture. Therefore, these objects make the images exhibit large intraclass variance and small interclass variance, which makes it very difficult to be recognized. In this study, an end-to-end attention-based semantic segmentation network (SSAtNet) is proposed. A pyramid attention pooling module is proposed to introduce the attention mechanism into the multiscale module for adaptive features refinement. To correct the detailed information, the pooling index correction module integrates pooling index maps from the encoder with high-level feature maps, which can help recover the fine-grained features. In the encoder phase, a more effective ResNet-101 backbone is designed to capture detailed features. What is more, a series of data augmentation methods are proposed to enhance the model's robustness. The proposed model is compared with several previous advanced networks and achieves the state of the art on the ISPRS Vaihingen dataset. The experiment results prove the effectiveness of the SSAtNet.