Geostatistical classification for remote sensing: an introduction

Traditional spectral classification of remotely sensed images applied on a pixel-by-pixel basis ignores the potentially useful spatial information bet ween the Values of proximate pixels. For some 30 years the spatial informat ion inherent in remotely sensed images has been employed, albeit by a limit ed number of researchers, to enhance spectral classification This has been achieved primarily by filtering the original imagery to (i) derive texture 'wavebands' for subsequent use in classification or (ii) smooth the imagery prior to (or after) classification. Recently the variogram has been used t o represent formally the spatial dependence in remotely sensed images and u sed in texture classification in place of simple variance filters. However, the variogram has also been employed in soil survey as a smoothing functio n for unsupervised classification. In this review paper, various methods of incorporating spatial information into the classification of remotely sens ed images are considered. The focus of the paper is on the variogram in cla ssification both as a measure of texture and as a guide to choice of smooth ing function. In the latter case, the paper focuses on the technique develo ped for soil survey and considers the modification that would be necessary for the remote sensing case. (C) 2000 Elsevier Science Ltd. All rights rese rved.