Spectral mixture analysis of AVIRIS-NG hyperspectral data for material identification and classification for the part of Kolkata city

Abstract

Identification and extraction of material in the urban scenario are always challenging due to the presence of different heterogeneous materials. Airborne hyperspectral sensors with the advantage of both high spectral and spatial resolution can be used in such areas for the identification and mapping of urban materials. Manual extraction of the endmembers in highly dense heterogeneous urban regions is a very difficult task to achieve, but with automated extraction techniques, it yields promising results. To counter the mixed pixels problem in heterogeneous urban areas automated endmember extraction techniques like Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA), and Count Based Endmember Selection (CoB) was used for pure endmember identification in the densely populated urban area of Kolkata region using AVIRIS NG imagery. Spectral Unmixing Multiple Endmember Spectral Mixture Analysis (MESMA), Linear Spectral Unmixing (LSU), and Mixture Tuned Matched Filtering (MTMF) was performed on AVIRIS NG data using the extracted endmembers of different urban materials. The rule-based classification was performed on abundance images obtained from MESMA, MTMF, and LSU techniques for comparative analysis. Classification using MESMA abundance images outperformed in mapping mixed urban environments with an overall accuracy (OA) of 88.6 % followed by the rule-based classified output of MTMF and LSU with OA of 75.27% and 72.45% respectively.