Abstract
Abstract. Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers viz., Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.
Highlights
Information on urban land cover types and their spatial distributions is essential for a wide range of studies in social sciences, demographics as well as by municipalities for developmental plans and decision making process (Stefanov et al, 2001)
The inverse Minimum Noise Fraction (MNF) transformation of the above bands resulted in 145 stable bands
The results indicated the importance of SWIR bands for identification of urban features
Summary
Information on urban land cover types and their spatial distributions is essential for a wide range of studies in social sciences, demographics as well as by municipalities for developmental plans and decision making process (Stefanov et al, 2001). It is an essential element for modelling and understanding the earth as a system (Lillesand et al, 2009). Land use and land cover classification accuracy in urban areas is limited owing to the broad spectral resolution of multispectral satellite data and spatial heterogeneity. Advances in imaging spectrometers have begun to fill the gap in multispectral sensor limitations (Heiden et al, 2007)
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