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Feature identification and extraction of urban built-up surfaces and materials in AVIRIS-NG hyperspectral imagery

Abstract 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.