Complex scene classification of remote sensing images based on CNN

doi:10.6046/gtzyyg.2018.04.08

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Abstract

Complex scene classification has great significance for mining the value information in remote sensing images. The proposed convolutional neural networks (CNN) can improve the complex scene classification of remote sensing images. The CNN method extracts features automatically, avoiding problems in the image pretreatment and the feature extraction by manual labor. An eight-layer CNN model is constructed in this paper, and the pre-treatment module has enhanced the adaptability of this method. Given the problem in choosing classifiers, this paper provides the Softmax and support vector machine (SVM) in the presented CNN. The experiment results in two datasets, the UC Merced Land Use and the Google of SIRI-WHU indicate that the presented CNN method can increase the accuracy of classification by more than 2% compared with the CNN with Overfeat feature method and the SRSCNN method, and the total classification accuracy of the two classifiers is over 95%.

Keywords convolutional neutral networks deep learning remote sensing images scene classification support vector machine

TP79

Issue Date: 07 December 2018

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	Kang ZHANG
	Baoqin HEI
	Shengyang LI
	Yuyang SHAO

Cite this article:

Kang ZHANG,Baoqin HEI,Shengyang LI, et al. Complex scene classification of remote sensing images based on CNN[J]. Remote Sensing for Land & Resources, 2018, 30(4): 49-55.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.04.08 OR https://www.gtzyyg.com/EN/Y2018/V30/I4/49

Fig.1 Overall flow chart of scene classification for remote sensing images based on CNN

Fig.2 CNN model for scene classification of remote sensing images

Fig.3 Scene classification method of remote sensing images based CNN

Fig.4 Datasets of UC Merced Land Use remote sensing images

Fig.5 Datasets of Google of SIRI-WHU remote sensing images

Fig.6 Classification results of remote sensing images

Fig.7 Confusion matrix of scene classification for remote sensing images based CNN

Fig.8 Similarity and difference

Tab.1 Classification accuracy of UC Merced Land Use dataset by different methods(%)

Tab.2 Classification accuracy of Google of SIRI-WHU dataset by different methods(%)

Fig.9 Curve of accuracy and loss function

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