TY - JOUR
T1 - Remote Sensing Image Scene Classification Based on Deep Learning Feature Fusion
AU - Wang , Liqi
AU - Zhang , Cheng
AU - Hou , Yuchao
AU - Tan , Xiuhui
AU - Cheng , Rong
AU - Gao , Xiang
AU - Bai , Yanping
JO - Journal of Information and Computing Science
VL - 1
SP - 65
EP - 80
PY - 2024
DA - 2024/03
SN - 19
DO - http://doi.org/10.4208/JICS-2024-005
UR - https://global-sci.org/intro/article_detail/jics/23880.html
KW - Image classification, Convolutional Neural Network (CNN), Grey Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), migration learning, Support Vector Machine (SVM).
AB - <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>