A Grad-CAM and capsule network hybrid method for remote sensing image scene classification

IF 1.8 4区 地球科学 Q3 GEOSCIENCES, MULTIDISCIPLINARY Frontiers of Earth Science Pub Date : 2024-07-04 DOI:10.1007/s11707-022-1079-x
Zhan He, Chunju Zhang, Shu Wang, Jianwei Huang, Xiaoyun Zheng, Weijie Jiang, Jiachen Bo, Yucheng Yang
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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.

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用于遥感图像场景分类的 Grad-CAM 和胶囊网络混合方法
遥感图像场景分类和遥感技术应用是研究热点。虽然基于 CNN 的模型已经达到了较高的平均准确率,但仍有一些类被误分类,如 "高速公路"、"闲置住宅 "和 "商业区"。这些类别包含典型的决定性特征、空间相关特征以及混合决定性特征和空间相关特征,从而限制了高质量的图像场景分类。针对这一问题,本文提出了一种用于图像场景分类的 Grad-CAM 和胶囊网络混合方法。Grad-CAM 和胶囊网络结构分别具有识别决定性特征和空间相关特征的潜力。通过使用预训练模型、混合结构和结构调整,所提出的模型可以识别决定性特征和空间相关特征。我们在三个分类难度不断增加的流行数据集上设计了一组实验。在最先进的实验中,平均准确率达到了 92.67%。具体来说,"教堂"、"宫殿 "和 "商业区 "类别的准确率分别为 83%、75% 和 86%。这项研究表明,混合结构可以通过同时考虑决定性特征和空间相关特征来有效提高性能。因此,Grad-CAM-CapsNet 是一种用于图像场景分类的前景广阔且功能强大的结构。
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来源期刊
Frontiers of Earth Science
Frontiers of Earth Science GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
3.50
自引率
5.00%
发文量
627
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
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