Remote Sensing Land Cover Classification Based on Lightweight Semantic Segmentation Network
High-resolution remote sensing images have rich spatial features.To solve the problems of complex models,blurred boundaries,and multi-scale segmentation in remote sensing land cover methods,this study proposes a lightweight semantic segmentation network based on boundary and multi-scale information.First,the method uses a lightweight MobileNetV3 classifier and depthwise separable convolutions to reduce computation.Second,the method adopts top-down and bottom-up feature pyramid structures for multi-scale segmentation.Next,a boundary enhancement module is designed to provide rich boundary detail information for the segmentation task.Then,the method designs a feature fusion module to fuse boundary and multi-scale semantic features.Finally,the method applies cross-entropy and Dice loss functions to deal with the sample imbalance.The mean intersection over union of the WHDLD dataset reaches 59.64%,and the overall accuracy reaches 87.68%.The mean intersection over union of the DeepGlobe dataset reaches 70.42%,and the overall accuracy reaches 88.81%.The experimental results show that the model can quickly and effectively realize the land cover classification of remote sensing images.
万方数据
维普
