Preprint Article Version 2 This version not peer reviewed

Spectral and Spatial Cloud Detection Onboard for Hyperspectral Remote Sensing Image

Version 1 : Received: 30 April 2017 / Approved: 1 May 2017 / Online: 1 May 2017 (10:44:08 CEST)
Version 2 : Received: 15 November 2017 / Approved: 16 November 2017 / Online: 16 November 2017 (04:29:19 CET)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Remote Sens. 2018, 10, 152
DOI: 10.3390/rs10010152

Abstract

It is strongly desirable to accurately detect the clouds in hyperspectral images onboard before compression. However, conventional onboard cloud detection methods are not appropriate to all situation such as shadowed cloud or darken snow covered surfaces which are not identified properly in the NDSI test. In this paper, we propose a new spectral–spatial classification strategy to enhance the orbiting cloud screen performances obtained on hyperspectral images by integrating threshold exponential spectral angle map (TESAM), adaptive Markov random field (aMRF) and dynamic stochastic resonance (DSR). TESAM is performed to classify the cloud pixels coarsely based on spectral information. Then aMRF is performed to do optimal process by using spatial information, which improved the classification performance significantly. Some misclassification points still exist after aMRF processing because of the noisy data in the onboard environment. DSR is used to eliminate misclassification points in binary labeling image after aMRF. Taking level 0.5 data from hyperion as dataset, the average overall accuracy of the proposed algorithm is 96.28% after test. The method can provide cloud mask for the on-going EO-1 images and related satellites with the same spectral settings without manual intervention. The experiment indicate that the proposed method reveals better performance than the classical onboard cloud detection or current state-of-the-art hyperspectral classification methods.

Subject Areas

onboard cloud detecion; region of interest compression; themodynamic phase; spectral angle map; markov random field; dynamic stochastic resonance

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