Conference Papers

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Subject: search.filters.subject.Endmember initialization

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    Similarity measures in generating spectrally distinct targets
    (Institute of Electrical and Electronics Engineers Inc., 2020) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
    In multispectral and hyperspectral remote sensing, classification of pixels is obtained by means of spectral similarity of known field or library spectra to unknown image spectra. Endmember extraction is the most decisive task in hyperspectral image analysis. Endmember initialization algorithms (EIAs) play a key role and support endmember extraction algorithms (EEAs) in extracting near optimal set of endmembers. Though there are few endmember initialization techniques available, similarity measures are not explored in detail in target generation. Hence, in this paper, it is proposed to explore similarity measures in identifying spectrally distinct signatures to use them as initial endmembers. Individual similarity measures are combined to form hybrid similarity measures to confirm their effectiveness in generating spectrally distinct targets. Initial set of endmembers extracted by proposed algorithm are used for initializing classical EEA, the NFINDR, which is sensitive to endmember initialization, and their performance in final endmembers selection is verified. Experimental results on two hyperspectral data sets show the superior performance of the similarity based endmember initialization algorithm (SMEIA). © 2020 IEEE.
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    INFLUENCE OF THE DARKEST PIXEL ON ENDMEMBERS INITIALIZATION
    (Institute of Electrical and Electronics Engineers Inc., 2021) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.
    Endmember extraction is one of the necessary steps in hyperspectral data investigation. The eventual objective of hyperspectral data processing and analysis is to improve the accuracy of target identification, and the precise identification of endmembers is a challenging task. The several of the algorithms proposed for endmember extraction require knowledge on the targets for appropriate initialization. The endmember extraction algorithms (EEAs) rely on endmember initialization algorithms (EIAs) and in turn many EIAs also require the knowledge on targets of interest to begin the search process effectively. A comprehensive study on targets of interest to begin the search process is due expected. Therefore, in this paper, the concept of extreme or boundary pixels is explored to identify the targets of interest and the darkest pixel (a pixel with minimum length) is identified and proposed as a new target of interest to begin the endmember search process. The utility/influence of the proposed target of interest, i.e. the darkest pixel, is studied with that's of the brightest pixel (a pixel with maximum length) which has been in use as a popular target of interest for EIAs so far. An automatic target generation process (ATGP) and a similarity measures based endmember initialization algorithm (SMEIA) are adapted to test the proposed initialization strategy. The experimental results have revealed the usefulness of the darkest pixel as an additional target of interest in addition to the brightest pixel in searching for endmembers by EIAs. The strategy of choosing the darkest pixel enhanced the initial target knowledge and also improved the performance of the EIAs considerably. © 2021 IEEE.