INFERRING URBAN LAND USE FROM SATELLITE SENSOR IMAGES USING KERNEL-BASED SPATIAL RECLASSIFICATION

Abstract

Per-pixel classification algorithms are poorly equipped to monitor urban land use in images acquired by the current generation of high spatial resolution satellite sensors. This is because urban areas commonly comprise a complex spatial assemblage of spectrally distinct land-cover types. In this study, a technique is described that attempts to derive information on urban land use in two stages. The first involves classification of the image into broad land-cover types. In the second stage, referred to as spatial reclassification, the classified pixels are grouped into discrete land-use categories on the basis of both the frequency and the spatial arrangement of the land-cover labels within a square kernel. The application of this technique, known as SPARK (~~~tial Reclassification erne el), is demonstrated using a SPOT-1 HRV m ultispectral image of southeast London, England. Preliminary results indicate that SPARK can be used to distinguish quite subtle differences of land use in urban areas.