Abstract
The hyperspectral data endmember extraction plays a prominent role. In recent years, many researchers put their efforts to develop a new approach for endmember extraction from hyperspectral data. The endmember extraction algorithms especially used to discover the purest type of each spectrally distinct component on a scene. Endmember extraction process can be influenced by type of data, number of endmembers, number of pixels being processed, and number of spectral bands in data, used algorithms and also by the type of noise present in the data. The identified endmembers can be used in the further processing of identification and classification. Comparison of endmember extraction algorithms is a challenging task due to absence of unified criteria and unavailability of a standardized dataset to validate any new algorithm. Previously comparison of endmember extraction algorithm has been carried out on Landsat 4-5 data (Multispectral), Hyperion and the airborne visible/infrared imaging spectrometer (AVIRIS) Cuprite Hyperspectral dataset. In this paper, we have analyzed two widely used methods of endmember extraction the Sequential Maximum Angle Convex Cone (SMACC) and Pixel Purity Index (PPI) on AVIRIS-Next Generation (NG) hyperspectral dataset of Jhagadiya, Gujarat, Jasper Ridge and Hyperion dataset. From the experimental results, it is concluded that SMACC performs better than PPI.
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Acknowledgements
Authors would like to acknowledge DST-NISA, UGC SAP (II) DRS Phase-II and DST-FIST for providing technical support to Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India and also thanks for financial assistance under DST- NISA research fellowship for this work.
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Nalawade, D.B. et al. (2019). Hyperspectral Remote Sensing Image Analysis with SMACC and PPI Algorithms for Endmember Extraction. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1035. Springer, Singapore. https://doi.org/10.1007/978-981-13-9181-1_28
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DOI: https://doi.org/10.1007/978-981-13-9181-1_28
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