Semantic segmentation for remote sensing images based on an AD-HRNet model
Xue Yang, Xiang Fan, Mingjun Peng, Qingfeng Guan, Luliang Tang
Abstract
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.