Yao WangThis email address is being protected from spambots. You need JavaScript enabled to view it.
Dalian University of Finance and Economics, Dalian, 116622, China
Received: April 18, 2024 Accepted: May 27, 2024 Publication Date: July 10, 2024
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
With the immediate development of the high-resolution remote sensing technology, information contained in the remote sensing images has obtained an explosive growth. The increasingly fruitful information enables accurate surface feature recognition, which will strongly provide support for effective urban and rural planning design, such as green space layout and bus station selection. Hence, it has attracted increasing attention from academic and industrial communities. However, due to the large volume and complex property, existing network structures cannot effectively process those high-resolution remote sensing image and extract useful information as much as possible, which blocks the progress of corresponding fields. To deal with the challenges above, a novel DenseNet-based high-resolution remote sensing image surface feature recognition method is proposed. On the one hand, the proposed method is constructed on the basis of the DenseNet-121 model, whose ratio of accuracy to parameter quantity is relatively high, which can better keep up with the large volume. On the other hand, the proposed method designs a contrastive learning mechanism to further improve the ability to distinguish surface features at the same time, which helps address the challenge from complex property. Experimental results showcase the effectiveness and superior of the proposed method in comparison with several surface feature recognition methods.
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