Year: 2024
Author: Liqi Wang, Cheng Zhang, Yuchao Hou, Xiuhui Tan, Rong Cheng, Xiang Gao, Yanping Bai
Journal of Information and Computing Science, Vol. 19 (2024), Iss. 1 : pp. 65–80
Abstract
In view that traditional manual feature extraction method cannot effectively extract the overall deep image information, a new method of scene classification based on deep learning feature fusion is proposed for remote sensing images. First, the Grey Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) are used to extract the shallow information of texture features with relevant spatial characteristics and local texture features as well; second, the deep information of images is extracted by the AlexNet migration learning network, and a 256-dimensional fully connected layer is added as feature output while the last fully connected layer is removed; and the two features are adaptively integrated, then the remote sensing images are classified and identified by the Grid Search optimized Support Vector Machine (GS-SVM). The experimental results on 21 types of target data of the public dataset UC Merced and 7 types of target data of RSSCN7 produced average accuracy rates of 94.77% and 93.79%, respectively, showing that the proposed method can effectively improve the classification accuracy of remote sensing image scenes.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/JICS-2024-005
Journal of Information and Computing Science, Vol. 19 (2024), Iss. 1 : pp. 65–80
Published online: 2024-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 16
Keywords: Image classification Convolutional Neural Network (CNN) Grey Level Co-occurrence Matrix (GLCM) Local Binary Patterns (LBP) migration learning Support Vector Machine (SVM).