A Grad-CAM and capsule network hybrid method for remote sensing image scene classification
文献类型：期刊论文
| 作者 | He, Zhan3,4; Zhang, Chunju2; Wang, Shu1; Huang, Jianwei4; Zheng, Xiaoyun3; Jiang, Weijie4; Bo, Jiachen4; Yang, Yucheng4 |
| 刊名 | FRONTIERS OF EARTH SCIENCE
 |
| 出版日期 | 2024-07-04 |
| 卷号 | N/A |
| 关键词 | image scene classification CNN Gaad-CAM CapsNet DenseNet |
| DOI | 10.1007/s11707-022-1079-x |
| 产权排序 | 3 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | 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. |
| WOS关键词 | FUSION FRAMEWORK ; ATTENTION |
| WOS研究方向 | Geology |
| WOS记录号 | WOS:001263103100005 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206066]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhang, Chunju; Wang, Shu |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Minist Nat Resources, Key Lab Jianghuai Arable Land Resources Protect &, Hefei 230088, Peoples R China 3.Minist Nat Resources, Shenzhen Data Management Ctr Planning & Nat Resour, Key Lab Urban Land Resources Monitoring & Simulat, Shenzhen 518000, Peoples R China 4.Hefei Univ Technol, Sch Civil Engn, Hefei 230009, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Zhan,Zhang, Chunju,Wang, Shu,et al. A Grad-CAM and capsule network hybrid method for remote sensing image scene classification[J]. FRONTIERS OF EARTH SCIENCE,2024,N/A. |
| APA | He, Zhan.,Zhang, Chunju.,Wang, Shu.,Huang, Jianwei.,Zheng, Xiaoyun.,...&Yang, Yucheng.(2024).A Grad-CAM and capsule network hybrid method for remote sensing image scene classification.FRONTIERS OF EARTH SCIENCE,N/A. |
| MLA | He, Zhan,et al."A Grad-CAM and capsule network hybrid method for remote sensing image scene classification".FRONTIERS OF EARTH SCIENCE N/A(2024). |
入库方式： OAI收割
来源：地理科学与资源研究所
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