Frequency-based contextual classification and gray-level vector reduction for land-use identification

Abstract

Attemps to map land use directly from higher spatial resolution satellite date with conventional computer classification techniques have proven to be ineffective. This is due to two facts. First, land use is a cultural concept. What we see on remote sensing imagery is only the physical evidence of land use as represented by combinations of land-cover types. Second, conventional classifiers employ only spectral information on a single-pixel basis. A large amount of spatial information is thus ignored. In this research, a contextual classification method was developed to obtain land-use information. The number of gray-level vectors in multispectral space was reduced using a new data-reduction algorithm through rotating multispectral space into eigen space.