The distribution of urban built-up surfaces is a key indicator of the level of urbanization, which originates from several ecological effects. In this study, three new spectral indices i.e. New Impervious Index (NII), Road Detection Index (RDI) and New Roof Extraction Index (NREI) have been developed for detection of built-up (Level-1), road and roof surfaces (Level-2), respectively, followed by a separability analysis between spectrally confused urban land cover classes. The entire analysis is carried out using AVIRIS-NG and ground hyperspectral data of the Udaipur, Rajasthan region of India. The results of the analysis depict that the proposed indices show an overall average accuracy of 96.12%, 92.24%, and 94.77%, respectively, which are higher than the outcomes of different existing built-up indices. Further, the separability analysis also confirms that the proposed indices can prove to be effective for separating built-up surfaces (Level-1 and 2) from the spectrally similar background.

Regular monitoring and precise mapping of urban environment is required by various applications. In this paper, a new method is proposed, in which different combinations of feature bands have been utilized for extraction of built-up surfaces, sub-surfaces and materials in different levels (Level-1, 2 and 3) using AVIRIS-NG hyperspectral imagery of Jodhpur, Rajasthan region of India. Features identified in this study are based on spectral indices, major principal components and fractional abundances, in which first combination is developed using spectral indices and fractional abundances while second is made using spectral indices and major principal components and finally third using combination of all the aforesaid features. It is observed that the combined form of all the aforementioned features produces better extraction results than the other two while combination of spectral indices and fractional abundances may be more useful than the combined form of spectral indices and major principal components.