High-resolution remote sensing building extraction based on attention mechanism and DeepLabv3+

Zebin Si; Bin Zhou; Ben Wang; Xiya Wang; Luxiao Zhu

doi:10.1117/12.2656777

20 October 2022 High-resolution remote sensing building extraction based on attention mechanism and DeepLabv3+

Zebin Si, Bin Zhou, Ben Wang, Xiya Wang, Luxiao Zhu

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124510J (2022) https://doi.org/10.1117/12.2656777
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

For the high-resolution remote sensing building image, the object shape is irregular and the texture characteristics are similar to those in the foreground and background. Meanwhile, traditional manual methods spend considerable time extracting features. In this paper, a Deeplabv3+ algorithm is modified and improved to better meet the accuracy, massive, and real-time requirements in semantic segmentation of high-resolution remote-sensing building objects. First of all, the lightweight network MobileNetv2 is implemented to replace Xception as the backbone to extract the network, so as to reduce the number of network parameters and improve the training speed. Secondly, ASPP (Atrus Spatial Pyramid Pooling) obtains the depth effective feature layer and integrates three parallel channel attention mechanisms to improve the extraction effect of depth features. The newly proposed algorithm is tested on the Massachusetts dataset. The results show that the IOU and accuracy of evaluation indicators are increased by 8.31% and 2.11%, respectively, compared with the original Deeplabv3+ model. While reducing the number of model parameters and training time, the segmentation effect of the new algorithm has also been partially optimized.

Citation Download Citation

Zebin Si, Bin Zhou, Ben Wang, Xiya Wang, and Luxiao Zhu "High-resolution remote sensing building extraction based on attention mechanism and DeepLabv3+", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124510J (20 October 2022); https://doi.org/10.1117/12.2656777

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