Sokoine University of Agriculture

Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery

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dc.contributor.author Shao, Zhenfeng
dc.contributor.author Deng, Juan
dc.contributor.author Wang, Lei
dc.contributor.author Fan, Yewen
dc.contributor.author Sumari, Neema S.
dc.contributor.author Cheng, Qimin
dc.date.accessioned 2021-01-27T12:06:13Z
dc.date.available 2021-01-27T12:06:13Z
dc.date.issued 2017-03-26
dc.identifier.uri http://www.suaire.sua.ac.tz/handle/123456789/3367
dc.description.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. en_US
dc.language.iso en en_US
dc.subject remote sensing imagery; en_US
dc.subject fuzzy autoencode mode en_US
dc.subject cloud detection en_US
dc.title Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery en_US
dc.type Article en_US


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