ANALYZING THE SHAPE CHARACTERISTICS OF LAND USE CLASSES IN REMOTE SENSING IMAGERY
- 1ITU, Civil Engineering Faculty, 80626 Maslak Istanbul, Turkey
- 2Dept. of Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, UK
- 3School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
- 4Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Keywords: Land Use, Image Segmentation, Landscape Metrics, Shape Metrics, Image Classification
Abstract. Shape is an important aspect of spatial attributes of land use segments in remotely sensed imagery, but it is still rarely used as a component in land use classification or image-based land use analysis. This study aims to quantitatively characterize land use classes using shape metrics. The study is conducted in a case area located in south China, covering twelve scenes of SPOT-5 images. There were total ten metrics selected for the analysis, namely, Convexity (CONV), Solidity (SOLI), Elongation (ELONG), Roundness (ROUND), Rectangular Fitting (RECT), Compact (COMP), Form Factor (FORM), Square pixel metric (SqP), Fractal Dimension (FD), and Shape Index (SI). The last five metrics were used to measure the complexity of shape. Eight land use classes were investigated in the case area, namely, roads, cultivated lands, settlement places, rivers, ponds, forest and grass lands, reservoirs, and dams. The results show that all typical shape properties of land use segments can be well measured by shape metrics. We identified the land use classes whose values are significantly differentiated from most classes for each metric. Two of the five complexity metrics, FORM and SqP, were identified to be more effective in characterizing the complexity of land use classes. We finally selected six shape metrics and deduced the "Shape Metric Signatures" (SMS) of different land use classes. SMS can serve as accurate and predictive discriminators of land use classes within the study area. Our results show that SMS can clearly distinguish spectrally similar land use classes. The results will help to build a more accurate and intelligent object-oriented classification system for land use classes.