Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery
文献类型：期刊论文
| 作者 | Wang, Guoli1,2 ; Fan, Bin1; Xiang, Shiming1; Pan, Chunhong1
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| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2017-09-01 |
| 卷号 | 10期号:9页码:4104-4115 |
| 关键词 | Convolutional Neural Networks (Cnns) Mixed-resolution Representation Remote Sensing Scene Classification Vector Of Locally Aggregated Descriptors (Vlad) |
| DOI | 10.1109/JSTARS.2017.2705419 |
| 文献子类 | Article |
| 英文摘要 | Scene classification is one of the most important issues in remote sensing image processing. To obtain a high discriminative feature representation for an image to be classified, traditional methods usually consider to densely accumulate hand-crafted low-level descriptors (e.g., scale-invariant feature transform) by feature encoding techniques. However, the performance is largely limited by the hand-crafted descriptors as they are not capable of describing the rich semantic information contained in various remote sensing images. To alleviate this problem, we propose a novel method to extract discriminative image features from the rich hierarchical information contained in convolutional neural networks (CNNs). Specifically, the low-level and middle-level intermediate convolutional features are, respectively, encoded by vector of locally aggregated descriptors (VLAD) and then reduced by principal component analysis to obtain hierarchical global features; meanwhile, the fully connected features are average pooled and subsequently normalized to form new global features. The proposed encoded mixed-resolution representation (EMR) is the concatenation of all the above-mentioned global features. Due to the usage of encoding strategies (VLAD and average pooling), our method can deal with images of different sizes. In addition, to reduce the computational consumption in the training stage, we directly extract EMR from VGG-VD and ResNet pretrained on the ImageNet dataset. We show in this paper that CNNs pretrained on the natural image dataset are more easily applied to the remote sensing dataset when the local structure similarity between two datasets is higher. Experimental evaluations on the UC-Merced and Brazilian Coffee Scenes datasets demonstrate that our method is superior to the state of the art. |
| WOS关键词 | MULTISCALE ; SCALE |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000412626400025 |
| 资助机构 | National Natural Science Foundation of China(61403375 ; Priority Academic Program Development of Jiangsu Higher Education Institutions ; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology ; 61472119 ; 61573352 ; 61375024 ; 91338202 ; 91646207) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14526]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Guoli,Fan, Bin,Xiang, Shiming,et al. Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(9):4104-4115. |
| APA | Wang, Guoli,Fan, Bin,Xiang, Shiming,&Pan, Chunhong.(2017).Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(9),4104-4115. |
| MLA | Wang, Guoli,et al."Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.9(2017):4104-4115. |
入库方式： OAI收割
来源：自动化研究所
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