Buildings extraction of GF-2 remote sensing image based on multi-layer perception network
LU Qi1(), QIN Jun1, YAO Xuedong2, WU Yanlan1,3(), ZHU Haochen1
1. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China 2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430072, China 3. Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China
The task of extracting buildings with high-resolution remote sensing image plays an important role in urban planning and urbanization. In view of the problems of existing deep learning extraction methods, for example, the shallow features can’t been used effectively and small target information is easily lost, this paper proposes a multi-level perceptual network. This network uses dense connection mechanism to fully extract feature information, and constructs parallel structure to retain spatial information of different feature resolution and enhance feature information of different depth and scale in order to reduce the loss of detail feature. At the same time, the ASPP module is used to obtain the information of different receptive fields and extract the deep architectural features at different scales. The experimental results show that the overall accuracy of the proposed method is 97.19%, intersection over union is 74.33% and theF1 score is 85.43% in the buildings extraction of GF-2 remote sensing image, all of which are higher than those of the traditional method and other deep learning methods. In addition, buildings with multi-source remote sensing images still have good extraction effect, which reflects the practicability of the method presented in this paper.
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2021, Vol. 33 
), 秦军1, 姚雪东2, 吴艳兰1,3(
