| 摘 要： |
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. |
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