Using object-based hierarchical classification to extract land use land cover classes from high resolution satellite imagery in a complex urban area

Abstract

Producing land use and land cover (LULC) maps, particularly in complex urban areas, is one of the most important necessities in civil management programs and is an important research topic in satellite image analysis. High-resolution satellite images provide more opportunities for cost-benefit production of such information. This paper proposes a hierarchical LULC classification based on image objects that are created from multiresolution segmentation. A rule-based strategy is used to implement a step-by-step object-based land cover classification on a pan-sharpened IKONOS image taken from a complex urban region in Shiraz, Iran. A new spatial geometrical analysis for the reclassification of unclassified land cover objects is also utilized. After the initial classification, an object-based land use classification is implemented based on the land cover results and using conceptual, spatial, and geometrical modeling of the relationships between land use elements. Overall classification accuracy was 89 and 87% for land cover and land use approaches, respectively. In the best unclassified object analysis, ∼70% of unclassified objects were reclassified correctly. The hierarchical methodology proposed here results in fewer unclassified objects since a multistage classification process is utilized rather than the traditional one-pass classification.