Article: Research on remote sensing image classification method using two-stream convolutional neural network Journal: International Journal of Wireless and Mobile Computing (IJWMC) 2022 Vol.23 No.3/4 pp.250 - 255 Abstract: Owing to the lack of remote sensing image data set and no regional pertinence in terms of characteristics for classification, we have published the remote sensing image of some areas in Haikou City, Hainan Province, and made a HN-7 dataset, which has the regional characteristics specific of Hainan Province. The HN-7 dataset consists of seven classes, of which the construction site and dirt road categories appear in the public remote sensing dataset for the first time. Owing to the limited quantity of the HN-7 dataset, we decided to train a small convolutional neural network from scratch for the classification task, by using a three-layer two-stream network for improving the accuracy of the neural network model. Our model achieved 98.57% accuracy on the test set. We compared the accuracy of four common networks trained on HN-7, and the results showed that our model achieves the best performance. Inderscience Publishers - linking academia, business and industry through research

Title: Research on remote sensing image classification method using two-stream convolutional neural network

Authors: Kai Peng; Juan Hu; Siyu Liu; Fang Qu; Houqun Yang; Jing Chen

Addresses: School of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, China ' School of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, China ' School of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, China ' School of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, China ' School of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, China ' School of Computer Science and Technology, Hainan University, Haikou, Hainan 570228, China

Abstract: Owing to the lack of remote sensing image data set and no regional pertinence in terms of characteristics for classification, we have published the remote sensing image of some areas in Haikou City, Hainan Province, and made a HN-7 dataset, which has the regional characteristics specific of Hainan Province. The HN-7 dataset consists of seven classes, of which the construction site and dirt road categories appear in the public remote sensing dataset for the first time. Owing to the limited quantity of the HN-7 dataset, we decided to train a small convolutional neural network from scratch for the classification task, by using a three-layer two-stream network for improving the accuracy of the neural network model. Our model achieved 98.57% accuracy on the test set. We compared the accuracy of four common networks trained on HN-7, and the results showed that our model achieves the best performance.

Keywords: classification method; remote sensing image; two-stream convolutional; neural network.

DOI: 10.1504/IJWMC.2022.127587

International Journal of Wireless and Mobile Computing, 2022 Vol.23 No.3/4, pp.250 - 255

Received: 28 Sep 2021
Accepted: 23 Mar 2022
Published online: 12 Dec 2022 *

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Title: Research on remote sensing image classification method using two-stream convolutional neural network

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