@Article{JICS-19-1,
author = {Wang, Liqi and Zhang, Cheng and Yuchao, Hou and Xiuhui, Tan and Rong, Cheng and Xiang, Gao and Yanping, Bai},
title = {Remote Sensing Image Scene Classification Based on Deep Learning Feature Fusion},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {19},
number = {1},
pages = {65--80},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {1746-7659},
doi = {https://doi.org/10.4208/JICS-2024-005},
url = {https://global-sci.com/article/91654/remote-sensing-image-scene-classification-based-on-deep-learning-feature-fusion}
}