Semantic segmentation of remote sensing images based on attention mechanism and feature fusion

Abstract

In this article, we design this algorithm to perform the segmentation task of remote sensing images by using attention module and feature fusion to deal with the challenges of features with different dimensions, small-size objects and uncertain boundary segmentation in remote sensing images. The encoder uses a PVTv2 model to increase the feature information acquisition capability of the model, and several convolutional paths of the feature aggregation module can acquire different size information of the image. The deep network uses attention mechanisms to obtain features that are valid for the segmentation task. In the decoding phase, the feature fusion module fuses the shallow network detail information with the deep network semantic information. The experimental comparison on the ISPRS Potsdam dataset shows that pixel accuracy of the model in this paper achieves 88.59%, IoU, mIoU, and FWIoU are higher than some classical semantic segmentation networks, which effectively increases the accuracy of remote sensing image segmentation.