A unified model for the information content of remote sensing imagery

The information content in remote sensing imagery depends upon various factors such as spatial and radiometric resolutions, spatial scale of the features to be imaged, radiometric contrast between different target types, and the type and amount of noise present in the imagery. Various statistical and textural measures are used to characterize image information content, based upon which different image processing techniques are employed to quantify this parameter. Previous work in this area have resulted in three different approaches for quantifying the image information content, primarily based on interpretability, mutual information, and entropy. These approaches, although well refined, are difficult to apply to all types of remote sensing imagery. We have developed an approach based on the use of classification accuracy to quantify image information content, since loss of information occurs if pixels in the image are wrongly classified. Using this approach, we have separately developed negative exponential models relating information content to image parameters such as spatial and spectral resolution. In this paper, we describe a preliminary unified information content model and assess its performance using hyperspectral AVIRIS imagery. The model combines the effects of the above parameters and takes into account the interrelationships between them with respect to information contained within the image. Using the model, appropriate trade-offs between the parameters can be investigated for obtaining a specific value for image information content for a particular application.