Spectral and Spatial Cloud Detection Onboard for Hyperspectral Remote Sensing Image

It is strongly desirable to accurately detect the clouds in hyperspectral images onboard 1 before compression. However, conventional onboard cloud detection methods are not appropriate 2 to all situation such as shadowed cloud or darken snow covered surfaces which are not identified 3 properly in the NDSI test. In this paper, we propose a new spectral–spatial classification strategy to 4 enhance the orbiting cloud screen performances obtained on hyperspectral images by integrating 5 threshold exponential spectral angle map (TESAM), adaptive Markov random field (aMRF) and 6 dynamic stochastic resonance (DSR). TESAM is performed to classify the cloud pixels coarsely based 7 on spectral information. Then aMRF is performed to do optimal process by using spatial information, 8 which improved the classification performance significantly. Some misclassification points still exist 9 after aMRF processing because of the noisy data in the onboard environment. DSR is used to eliminate 10 misclassification points in binary labeling image after aMRF. Taking level 0.5 data from hyperion 11 as dataset, the average overall accuracy of the proposed algorithm is 96.28% after test. The method 12 can provide cloud mask for the on-going EO-1 images and related satellites with the same spectral 13 settings without manual intervention. The experiment indicate that the proposed method reveals 14 better performance than the classical onboard cloud detection or current state-of-the-art hyperspectral 15 classification methods. 16