CLASSIFICATION OF SATELLITE FUSED DATA FOR LAND USE MAPPING IN DEVELOPMENT PLAN

Abstract

Land use mapping in development plan basically provides resources of information and important tool in decision making. In relation to this, fine resolution of recent satellite remotely sensed data have found wide applications in land use/land cover mapping. This study reports on work carried out for classification of fused image for land use mapping in detail scale for Local Plan. The LANDSATTM, SPOT Pan and IKONOS satellite were fused and examined using three data fusion techniques, namely Principal Component Transfonn (PCT), Wavelet Transform and Multiplicative fusing approach. The best fusion technique for three datasets was determined based on the assessment of class separabilities and visualizations evaluation of the selected subset of the fused datasets, respectively. Principal Component Transform has been found to be the best technique for fusing the three datasets, where the best fused data set was subjected to further classification for producing level of land use classes while level II and III pass on to nine classes of detail classification for local plan. The overall data classification accuracy of the best fused data set was 0.86 (kappa statistic). Final land use output from classified data was successfully generated in accordance to local plan land use mapping for development plan purposes.