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Cloud Detection in Satellite Images Using an Immune Antibody Coding Algorithm

A novel image interpretation method for cloud detection under complex background was proposed utilizing the texture diversity of clouds and background in satellite remotely sensed images. The affinity formula of the training image's immune primitives was presented by statistical analysis, which bears an analogy with the lowest amino acids combinative energy according to the biological immune antibody coding principle, to achieve the finite image feature dimension by optimize combination. Furthermore, this methodology was employed in the cloud-contaminated area detection. The cloud antibody has been configured with two feature parameters of the fractal and angular second moment. A cloud detection algorithm has been designed and tested on 200 IKONOS satellite images with a detection rate of 97 percent which proved valid and robust. This immune antibody describing theory could be applied for pattern recognition and classification of satellite images under complex background.