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Article

Onboard Spectral and Spatial Cloud Detection for Hyperspectral Remote Sensing Images

1
School of Automation Science and Electrical Engineering, Beihang University, No.37 Xueyuan Road, Beijing 100191, China
2
China Academy of Space Technology (CAST), Beijing 100094, China
3
China Centre for Resources Satellite Data and Application, No.5 Fengxian East Road, Beijing 100094, China
4
School of Mathematics and Systems Science, Beihang University, No.37 Xueyuan Road, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 152; https://doi.org/10.3390/rs10010152
Received: 15 November 2017 / Revised: 15 January 2018 / Accepted: 16 January 2018 / Published: 20 January 2018
(This article belongs to the Section Remote Sensing Image Processing)
The accurate onboard detection of clouds in hyperspectral images before lossless compression is beneficial. However, conventional onboard cloud detection methods are not applicable all the time, especially for shadowed clouds or darkened snow-covered surfaces that are not identified in normalized difference snow index (NDSI) tests. In this paper, we propose a new spectral-spatial classification strategy to enhance the performance of an orbiting cloud screen obtained on hyperspectral images by integrating a threshold exponential spectral angle map (TESAM), adaptive Markov random field (aMRF) and dynamic stochastic resonance (DSR). TESAM is applied to roughly classify cloud pixels based on spectral information. Then aMRF is used to do optimal process by using spatial information, which improved the classification performance significantly. Nevertheless, misclassifications occur due to noisy data in the onboard environments, and DSR is employed to eliminate noise data produced by aMRF in binary labeled images. We used level 0.5 data from Hyperion as a dataset, and the average tested accuracy of the proposed algorithm was 96.28% by test. This method can provide cloud mask for the on-going EO-1 and related satellites with the same spectral settings without manual intervention. Experiments indicate that the proposed method has better performance than the conventional onboard cloud detection methods or current state-of-the-art hyperspectral classification methods. View Full-Text
Keywords: onboard cloud detection; region of interest compression; thermodynamic phase; spectral angle map; Markov random field; dynamic stochastic resonance onboard cloud detection; region of interest compression; thermodynamic phase; spectral angle map; Markov random field; dynamic stochastic resonance
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MDPI and ACS Style

Li, H.; Zheng, H.; Han, C.; Wang, H.; Miao, M. Onboard Spectral and Spatial Cloud Detection for Hyperspectral Remote Sensing Images. Remote Sens. 2018, 10, 152. https://doi.org/10.3390/rs10010152

AMA Style

Li H, Zheng H, Han C, Wang H, Miao M. Onboard Spectral and Spatial Cloud Detection for Hyperspectral Remote Sensing Images. Remote Sensing. 2018; 10(1):152. https://doi.org/10.3390/rs10010152

Chicago/Turabian Style

Li, Haoyang, Hong Zheng, Chuanzhao Han, Haibo Wang, and Min Miao. 2018. "Onboard Spectral and Spatial Cloud Detection for Hyperspectral Remote Sensing Images" Remote Sensing 10, no. 1: 152. https://doi.org/10.3390/rs10010152

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