Zhan He, Chunju Zhang, Shu Wang, Jianwei Huang, Xiaoyun Zheng, Weijie Jiang, Jiachen Bo, Yucheng Yang
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引用次数: 0
Abstract
Remote sensing image scene classification and remote sensing technology applications are hot research topics. Although CNN-based models have reached high average accuracy, some classes are still misclassified, such as “freeway,” “spare residential,” and “commercial_area.” These classes contain typical decisive features, spatial-relation features, and mixed decisive and spatial-relation features, which limit high-quality image scene classification. To address this issue, this paper proposes a Grad-CAM and capsule network hybrid method for image scene classification. The Grad-CAM and capsule network structures have the potential to recognize decisive features and spatial-relation features, respectively. By using a pre-trained model, hybrid structure, and structure adjustment, the proposed model can recognize both decisive and spatial-relation features. A group of experiments is designed on three popular data sets with increasing classification difficulties. In the most advanced experiment, 92.67% average accuracy is achieved. Specifically, 83%, 75%, and 86% accuracies are obtained in the classes of “church,” “palace,” and “commercial_area,” respectively. This research demonstrates that the hybrid structure can effectively improve performance by considering both decisive and spatial-relation features. Therefore, Grad-CAM-CapsNet is a promising and powerful structure for image scene classification.
期刊介绍:
Frontiers of Earth Science publishes original, peer-reviewed, theoretical and experimental frontier research papers as well as significant review articles of more general interest to earth scientists. The journal features articles dealing with observations, patterns, processes, and modeling of both innerspheres (including deep crust, mantle, and core) and outerspheres (including atmosphere, hydrosphere, and biosphere) of the earth. Its aim is to promote communication and share knowledge among the international earth science communities