DSDN-Net: An Effective Network for Semantic Segmentation in Open-Pit Coal Mining Areas for Land Cover Recognition

Abstract

Currently, existing methods in land cover recognition in open-pit coal mining areas face the issue of insufficient accuracy due to multiscale and blurred boundaries when processing remote sensing images. This paper introduces a remote sensing image semantic segmentation network, DSDN-Net, to tackle the issues. DSDN-Net adopts MobileNetV2 as the backbone, and a spatial pyramid pooling structure, Dynamic Snake Dense-ASPP (DSDN-ASPP), which enhances the model’s receptive field based on cross-layer connections is designed to increase the model’s receptive field by introducing dynamic snake convolution and utilizing depthwise convolutions with different kernel sizes, allowing the model to focus more on spatial features in remote sensing images. To handle blurred boundaries in remote sensing image, the decoder of DSDN-Net incorporates a Convolutional Block Attention Module (CBAM) to enhance precision. A dataset containing 5440 remote sensing images from the open-pit coal mining area is constructed using high-resolution remote sensing image. Experimental results on the open-pit coal mining area dataset demonstrate that the proposed DSDN-Net outperforms existing methods in multiple performance metrics.