Urban Land Use/Land Cover Discrimination Using Image-Based Reflectance Calibration Methods for Hyperspectral Data

Irrespective of substantial research in land use/land cover (LULC) monitoring of urban area, hyperspectral data is not yet exploited effectively because of lack of local spectral resources and a practical reflectance calibration method. The objective of this research is to develop an effective methodology for urban LULC classification using image-based reflectance calibration methods: especially Vegetation-Impervious-Soil classes (VIS), using hyperspectral data. We used EO-1 Hyperion image of Pune City, India and assessed the suitability of different land covers as reflectance calibration surfaces. Furthermore, we performed LULC classification using different reflectance calibration methods such as Internal Area Relative Reflectance, Flat Field Relative Reflectance, and 6S for comparative analysis. Urban VIS signatures extracted from Hyperion image show distinct spectral curves at broader level. Flat Field Relative Reflectance method provides above 90 percent average overall accuracy. An advanced physics-based method such as 6S does not provide any added advantage over image-based calibration methods.