Semantic Segmentation of Remote Sensing Images Using the Channel Domain Attention Mechanism Deeplabv3+ Algorithm

XU Chang-you, FAN Shao-sheng, ZHU Hang

Control Engineering of China ›› 2023, Vol. 30 ›› Issue (2) : 368-375.

Control Engineering of China ›› 2023, Vol. 30 ›› Issue (2) : 368-375.

Semantic Segmentation of Remote Sensing Images Using the Channel Domain Attention Mechanism Deeplabv3+ Algorithm

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Abstract

In order to extract typical ground objects of high-resolution remote sensing images, and to solve the problems of fuzzy edge segmentation in Deeplabv3+ remote sensing image segmentation, such as holes and missing classification, a method of adding channel attention mechanism module to Deeplabv3+ is proposed based on deep learning to enhance segmentation results. First, the high-level feature map obtained through the deep convolutional network is input to the channel attention mechanism, and pixel-level feature enhancement between channels is performed. Then the multi-scale input image is obtained through the spatial pyramid pool, and the category imbalance corrected to extract the completed segmentation information of the image realizes the optimization of segmentation boundary information. Finally, by collecting remote sensing images of Guangzhou City Gaofen No. 2 for remote sensing data processing, labeling, and after enhancement, experiments are carried out and compared with classic semantic segmentation networks U-Net, SegNet, and PSPNet. The results show that the evaluation index MIOU of this method reaches by 96.19%, MPA reaches by 97.85%. 

Key words

Deep learning / semantic segmentation / attention mechanism / Deeplabv3 + / remote sensing image

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XU Chang-you, FAN Shao-sheng, ZHU Hang. Semantic Segmentation of Remote Sensing Images Using the Channel Domain Attention Mechanism Deeplabv3+ Algorithm[J]. Control Engineering of China, 2023, 30(2): 368-375

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