Cloud detection method of high spatial resolution remote sensing data combining object-oriented technique and GURLS classifier

doi:10.13474/j.cnki.11-2246.2019.0160

Abstract

Abstract:

Cloud is an important factor in remote sensing information acquisition. With the application of domestic high spatial resolution satellite data, accurate cloud detection has important significance to the ground information effective acquisition. In the paper, GF-1 and GF-2 multi-spectral images are used as data source to obtain homogenous objects by image segmentation firstly. Then based on spectral features, texture features, and geometrical features-9 features, a rule set is established. With the rule set as input, the GURLS classifier is used to detect cloud combined with threshold method. Applied on high resolution data with different time and scenarios, the method is compared with the pixel-based maximum likelihood method and SVM method. The result shows that the proposed method has a cloud extraction accuracy of over 95% and a Kappa coefficient of over 0.9.

Key words: cloud detection, object-oriented, GURLS, high spatial resolution, remote sensing images

CLC Number:

P237

YIN Yaqiu, LENG Yue, ZHAO Yuling, AN Na, JU Xing. Cloud detection method of high spatial resolution remote sensing data combining object-oriented technique and GURLS classifier[J]. 测绘通报, 2019, 0(5): 109-112,142.

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