Effects of uncorrelated and correlated noise on image information content

The information content in remote sensing imagery depends upon various factors such as spatial and radiometric resolutions, radiometric contrast between different target types, and also the final application for which the imagery has been acquired. Our approach to quantifying image information content is based upon classification accuracy. As noise is added to the image, the classification accuracy reduces, thereby resulting in loss of "information". The relationship between the information content and the noise variance can be described by a negative exponential model. The model is seen to be applicable for relating the information content to noise variance for Landsat TM as well as multi-look and single-look SIR-C imagery. We observe that the relationship is independent of the type of noise (Gaussian, Rayleigh, or gamma). However, the rate of information loss increases with the correlation distance in the case of spatially correlated noise. The rate of information loss also increases with the number of classes chosen for classifying the scene. The model is useful in deducing allowable signal-to-noise ratios for different sensor systems.