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.
Highlights
As hyperspectral remote sensing technologies progress, hyperspectral imaging techniques [1] are being widely used in many fields such as meteorology, earth observations and military affairs
We demonstrate a cloud detection algorithm that mainly uses a threshold exponential spectral angle map (TESAM), adaptive Markov random field and dynamic stochastic resonance (DSR)
Hyperspectral images were proposed by TESAM, which provided the basic classification result, and adaptive Markov random field (aMRF) was used based on the classification
Summary
As hyperspectral remote sensing technologies progress, hyperspectral imaging techniques [1] are being widely used in many fields such as meteorology, earth observations and military affairs. Earth observation satellites primarily sense changes in earth surfaces due to city planning, geological prospecting, military reconnaissance and natural disasters. Regardless of the application background, most remote sensing images contain clouds that, especially in the visible and infrared range, strongly affect the received electromagnetic radiation. Clouds cover approximately 70% of the earth’s surface [2] and play a dominant role in the energy and water cycles of our planet. The earth’s radiative budget or aerosol detection as influenced by clouds is not the focus of this paper
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