A spatial processing algorithm to reduce the effects of mixed pixels and increase the separability between classes

Abstract Mixed pixels are an important problem in the identification and proportion estimation of agriculturally important crops in Landsat satellite scenes. The spectral response of mixed pixels is influenced by more than one ground cover type which decreases the separability of component crop classes and hence degrades the performance of classification procedures. An algorithm called CASCADE is described which is based on spatial information and consideration of a linear mixing model. The CASCADE procedure provides a means for allocating a pixel to one of the surrounding, more “homogeneous” regions which it most closely resembles. Processing all pixels in an image with CASCADE before classification significantly increases the separability between crop classes as well as the precision of the crop proportion estimates.