Dependence of Image Information Content on Gray-scale Resolution

Remote sensing images acquired in various spectral bands are used to estimate certain geophysical parameters or detect the presence or extent of geophysical phenomena. In a majority of cases, the raw image acquired by the sensor is processed using various operations such as filtering, compression, enhancement, etc. In performing these operations, the analyst is attempting to maximise the information content in the image to fulfill the end objective. The information content in a remote sensing image for a specific application is greatly dependent on the gray-scale resolution of the image. One of the measures to quantify information content is classification accuracy. In such applications, the loss in information content as a result of degraded gray-scale resolution may not be significant. This is because, although the value of a pixel may change as a result of the decrease in the resolution, the same pixel may, in most cases, be correctly classified. Our research reveals that the loss in information is exponential with respect to the number of gray levels. The model is seen to be applicable for Landsat TM and SIR-C images. Using our mathematical model for the information content of images as a function of gray-scale resolution, one can specify an “optimal” gray-scale resolution for an image. Introduction and Background Digital images acquired by various sensors in remote sensing applications are used to estimate certain parameters or detect the presence or extent of specific phenomena. Examples in the geophysical domain include the estimation of soil moisture or the delineation of the ice-water boundary in polar regions using synthetic aperture radar imagery. In a majority of cases, the raw image acquired by the sensor is processed using various operations such as filtering, compression, enhancement, etc. In all of these cases, the analyst is attempting to assess and maximise the information content in the image to fulfill the end objective. While this appears deceptively simple, there are a variety of issues that need to be addressed in order that the available information content in imagery is enhanced for a specific purpose. One needs to quantify the information content of an image before one attempts to perform operations to increase this parameter. In general, image information content is a function of several variables, including resolution (both gray-scale as well as spatial), scale of variability of the physical parameter of interest, image statistics, etc. (Dowman and Peacegood, 1989; Kalmykov et al., 1989; Blacknell and Oliver, 1993). It is important to recognize that the same image may contain different information content values depending on the objective. For example, consider an optical image of a scene containing different types of terrain. Despite the poor grayscale resolution of the image, it may still be useful in classifying different terrain types, as long as the radiometric differences between the means of the individual terrain types are larger than the gray-scale resolution. We can then say that the information content of the image for classifying terrain types is high. On the other hand, if it is desired to estimate the amount of soil moisture in a bare soil area with