OBJECT-BASED CLASSIFICATION OF IKONOS DATA FOR RURAL LAND USE MAPPING

Land uses in rural areas of Japan, which typically consist of small agricultural fields, complex vegetation covers, and sparsely distributed residential areas, have been problematic in terms of land use mapping using satellite remote sensing data due to the complexity of the spatial structure. Increasing availability of very high resolution satellite (VHRS) data and the advancement of object-based image classification techniques in recent years provide a new opportunity for mapping detailed land uses from space. To utilize VHRS data and object-based image classification for creating land use maps, the correspondence between image objects extractable from VHRS data and land use classes observable on the ground, based on the spectral, spatial, and contextual relationships among image objects, must be established, because neither pixels nor segmented image objects of VHRS data directly correspond to land use classes into which we want to classify geo-spatial features on the ground. In this study, we conducted quantitative analyses of the spectral and spatial properties of image objects extracted from an IKONOS pan-sharpen image of a rural town in southwestern Japan, and proposed a classification framework for detailed land use mapping. Firstly, we extracted image objects from the IKONOS data using a multi-scale segmentation technique. Based on the statistical analyses of image objects, a hierarchical classification scheme for object-oriented land use classification was proposed