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Article

Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100096, China
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ISPRS Int. J. Geo-Inf. 2020, 9(10), 571; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100571
Received: 13 August 2020 / Revised: 18 September 2020 / Accepted: 27 September 2020 / Published: 29 September 2020
(This article belongs to the Special Issue Geospatial Artificial Intelligence)
Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network’s ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively. View Full-Text
Keywords: remote sensing image; semantic segmentation; fully convolutional network; convolutional neural network; self-attention mechanism remote sensing image; semantic segmentation; fully convolutional network; convolutional neural network; self-attention mechanism
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MDPI and ACS Style

Li, J.; Xiu, J.; Yang, Z.; Liu, C. Dual Path Attention Net for Remote Sensing Semantic Image Segmentation. ISPRS Int. J. Geo-Inf. 2020, 9, 571. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100571

AMA Style

Li J, Xiu J, Yang Z, Liu C. Dual Path Attention Net for Remote Sensing Semantic Image Segmentation. ISPRS International Journal of Geo-Information. 2020; 9(10):571. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100571

Chicago/Turabian Style

Li, Jinglun, Jiapeng Xiu, Zhengqiu Yang, and Chen Liu. 2020. "Dual Path Attention Net for Remote Sensing Semantic Image Segmentation" ISPRS International Journal of Geo-Information 9, no. 10: 571. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100571

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