Semantic segmentation for remote sensing images based on an AD-HRNet model

Semantic segmentation for remote sensing images faces challenges of unbalanced category weight, rich context causing difficulties of recognition, blurred boundaries of multi-scale objects, and so on. To address these problems, we propose a new model by combining HRNet with attention mechanisms and dilated convolution, denoted as: AD-HRNet for the semantic segmentation of remote sensing images. In the framework of AD-HRNet, we obtained the weight value of each category based on an improved weighted cross-entropy function by introducing the median frequency balance method to solve the issue of class weight imbalance. The Shuffle-CBAM module with channel attention and spatial attention in AD-HRNet framework was applied to extract more global context information of images through slightly increasing the amount of computation. To address the problem of blurred boundaries caused by multi-scale object segmentation and edge segmentation, we developed an MDC-DUC module in AD-HRNet framework to capture the context information of multi-scale objects and the edge information of many irregular objects. Taking Postdam, Vaihingen, and SAMA-VTOL datasets as materials, we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation models. Experimental results shown that AD-HRNet increases the mIoUs to 75.59% and 71.58% based on the Postdam and Vaihingen datasets, respectively.