MCSGNet: A Encoder–Decoder Architecture Network for Land Cover Classification
Kai Hu, Enwei Zhang, Xin Dai, Min Xia, Fenghua Zhou, Liguo Weng, Haifeng Lin
Abstract
The analysis of land cover types is helpful for detecting changes in land use categories and evaluating land resources. It is of great significance in environmental monitoring, land management, land planning, and mapping. At present, remote sensing imagery obtained by remote sensing is widely employed in the classification of land types. However, most of the existing methods have problems such as low classification accuracy, vulnerability to noise interference, and poor generalization ability. Here, a multi-scale contextual semantic guidance network is proposed for the classification of land cover types by deep learning. The whole model combines an attention mechanism with convolution to make up for the limitation that the convolution structure can only focus on local features. In the process of feature extraction, an interactive structure combining attention and convolution is introduced in the deep layer of the network to fully extract the abstract information. In this paper, the semantic information guidance module is introduced in the cross-layer connection part, ensuring that the semantic information between different levels can be used for mutual guidance, which is conducive to the classification process. A multi-scale fusion module is proposed at the decoder to fuse the features between different layers and avoid loss of information during the recovery process. Experiments on two public datasets demonstrate that the suggested approach has higher accuracy than existing models as well as strong generalization ability.