Laser & Optoelectronics Progress, Volume. 58, Issue 24, 2428006(2021)

Building Extraction from Remote Sensing Imagery Based on Scale-Adaptive Fully Convolutional Network

Fan Feng1, Shuangting Wang1, Jin Zhang1, Chunyang Wang1、*, and Bing Liu2
Author Affiliations
  • 1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, China
  • 2PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan 450001, China
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    The present research proposes an efficient scale-adaptive and fully convolutional network based on an encoder-decoder network, which represents a crucial innovation aimed at improving buildings extraction with various scales from remote sensing imagery with high spatial resolution. First, a multiple-input multiple-output structure is proposed to obtain multiscale features fusion and cross-scale aggregation. Then, a residual pyramid pooling module is deployed to learn deep adaptive multiscale features. Finally, the initial aggregated features are further processed using a residual dense-connected aggregated-feature refinement module. Pixel dependencies of different feature maps are investigated to improve the classification accuracy. Experimental results on the WHU aviation and the Massachusetts datasets show that compared with other methods, the method has a better extraction effect on buildings, and the training time and memory usage are moderate, which has high practical value.

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    Fan Feng, Shuangting Wang, Jin Zhang, Chunyang Wang, Bing Liu. Building Extraction from Remote Sensing Imagery Based on Scale-Adaptive Fully Convolutional Network[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2428006

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Jan. 6, 2021

    Accepted: Jan. 20, 2021

    Published Online: Dec. 3, 2021

    The Author Email: Wang Chunyang (wcy@hpu.edu.cn)

    DOI:10.3788/LOP202158.2428006

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