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Remote Sens. 2017, 9(4), 311; doi:10.3390/rs9040311

Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery

1
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
3
Department of Informatics, Fuculty of Science, Sokoine University of Agriculture (SUA), P.O. Box 3038, Morogoro, Tanzania
4
School of Electronics Information and Communications, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 16 January 2017 / Revised: 9 March 2017 / Accepted: 23 March 2017 / Published: 26 March 2017
View Full-Text   |   Download PDF [11691 KB, uploaded 26 March 2017]   |  

Abstract

Cloud detection of remote sensing imagery is quite challenging due to the influence of complicated underlying surfaces and the variety of cloud types. Currently, most of the methods mainly rely on prior knowledge to extract features artificially for cloud detection. However, these features may not be able to accurately represent the cloud characteristics under complex environment. In this paper, we adopt an innovative model named Fuzzy Autoencode Model (FAEM) to integrate the feature learning ability of stacked autoencode networks and the detection ability of fuzzy function for highly accurate cloud detection on remote sensing imagery. Our proposed method begins by selecting and fusing spectral, texture, and structure information. Thereafter, the proposed technique established a FAEM to learn the deep discriminative features from a great deal of selected information. Finally, the learned features are mapped to the corresponding cloud density map with a fuzzy function. To demonstrate the effectiveness of the proposed method, 172 Landsat ETM+ images and 25 GF-1 images with different spatial resolutions are used in this paper. For the convenience of accuracy assessment, ground truth data are manually outlined. Results show that the average RER (ratio of right rate and error rate) on Landsat images is greater than 29, while the average RER of Support Vector Machine (SVM) is 21.8 and Random Forest (RF) is 23. The results on GF-1 images exhibit similar performance as Landsat images with the average RER of 25.9, which is much higher than the results of SVM and RF. Compared to traditional methods, our technique has attained higher average cloud detection accuracy for either different spatial resolutions or various land surfaces. View Full-Text
Keywords: remote sensing imagery; fuzzy autoencode mode; cloud detection remote sensing imagery; fuzzy autoencode mode; cloud detection
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Shao, Z.; Deng, J.; Wang, L.; Fan, Y.; Sumari, N.S.; Cheng, Q. Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery. Remote Sens. 2017, 9, 311.

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