Geostatistical estimation of resolution-dependent variance in remotely sensed images

The variance of a remotely sensed image is determined by the interaction of scene properties with the spatial characteristics of the sensor. image var iance is related to information content, and therefore determines the abili ty to extract useful information about scene conditions. Mie describe a tec hnique to estimate image variance at multiple spatial resolutions. The meth od is useful for comparing the capabilities of sensors with differing spati al responses. The point-spread function (PSF) and the variogram quantify the spatial char acteristics of the sensor and image, respectively. A geostatistical model b ased on these two elements relates the punctual variogram of a scene with t he regularized variogram of an image. This model forms the basis for a nume rical approach to approximate the punctual variogram from regularized obser vations. The resulting estimate of the punctual variogram allows analytical determination of image variance at different spatial resolutions. Analysis of simulated images confirms the utility of this algorithm. Varian ce of coarse-resolution images may be estimated reliably from fine-resoluti on data. Simulations of multiscale variability show that the method handles more complex types of scene variability as well. The geostatistical varian ce estimation algorithm better characterizes the relationship between varia nce and spatial resolution than do simpler methods, such as averaging block s of pixels. Specifically, methods which do not account for overlap of adja cent placements of the sensor PSF tend to overestimate the variance of the resulting images. The algorithm presented here can be used to evaluate the utility of different sensors for particular applications, when the relation ship between spatial resolution and image information content is important.