Geostatistical classi®cation for remote sensing: an introduction

Traditional spectral classi®cation of remotely sensed images applied on a pixel-by-pixel basis ignores the potentially useful spatial information between the values of proximate pixels. For some 30 years the spatial information inherent in remotely sensed images has been employed, albeit by a limited number of researchers, to enhance spectral classi®cation. This has been achieved primarily by ®ltering the original imagery to (i) derive texture `wavebands' for subsequent use in classi®cation or (ii) smooth the imagery prior to (or after) classi®cation. Recently, the variogram has been used to represent formally the spatial dependence in remotely sensed images and used in texture classi®cation in place of simple variance ®lters. However, the variogram has also been employed in soil survey as a smoothing function for unsupervised classi®cation. In this review paper, various methods of incorporating spatial information into the classi®cation of remotely sensed images are considered. The focus of the paper is on the variogram in classi®cation both as a measure of texture and as a guide to choice of smoothing function. In the latter case, the paper focuses on the technique developed for soil survey and considers the modi®cation that would be necessary for the remote sensing case. 7 2000 Elsevier Science Ltd. All rights reserved.