Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet

doi:10.3390/app9102028

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	Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
	X.Zhang; Y.C.Wang; N.Zhang; D.D.Xu; B.Chen
	2019
发表期刊	Applied Sciences-Basel
卷号	9 期号:10 页码:26
摘要	One of the challenges in the field of remote sensing is how to automatically identify and classify high-resolution remote sensing images. A number of approaches have been proposed. Among them, the methods based on low-level visual features and middle-level visual features have limitations. Therefore, this paper adopts the method of deep learning to classify scenes of high-resolution remote sensing images to learn semantic information. Most of the existing methods of convolutional neural networks are based on the existing model using transfer learning, while there are relatively few articles about designing of new convolutional neural networks based on the existing high-resolution remote sensing image datasets. In this context, this paper proposes a multi-view scaling strategy, a new convolutional neural network based on residual blocks and fusing strategy of pooling layer maps, and uses optimization methods to make the convolutional neural network named RFPNet more robust. Experiments on two benchmark remote sensing image datasets have been conducted. On the UC Merced dataset, the test accuracy, precision, recall, and F1-score all exceed 93%. On the SIRI-WHU dataset, the test accuracy, precision, recall, and F1-score all exceed 91%. Compared with the existing methods, such as the most traditional methods and some deep learning methods for scene classification of high-resolution remote sensing images, the proposed method has higher accuracy and robustness.
关键词	convolutional neural network,ResNet,semantic information,remote,sensing images,scene classification,TensorFlow,satellite images,deep,representation,network,features,scale,Chemistry,Engineering,Materials Science,Physics
DOI	10.3390/app9102028
收录类别	SCI
语种	英语
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/62783
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	X.Zhang,Y.C.Wang,N.Zhang,et al. Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet[J]. Applied Sciences-Basel,2019,9(10):26.
APA	X.Zhang,Y.C.Wang,N.Zhang,D.D.Xu,&B.Chen.(2019).Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet.Applied Sciences-Basel,9(10),26.
MLA	X.Zhang,et al."Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet".Applied Sciences-Basel 9.10(2019):26.

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