onboard spectral and spatial cloud detection for hyperspectral remote sensing images

Clicks: 170
ID: 259464
2018
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Abstract
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.
Reference Key
li2018remoteonboard Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Haoyang Li;Hong Zheng;Chuanzhao Han;Haibo Wang;Min Miao
Journal Journal of pharmacological sciences
Year 2018
DOI
10.3390/rs10010152
URL
http://www.mdpi.com/2072-4292/10/1/152
Keywords
dynamic stochastic resonancescience

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