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- W4312405917 abstract "We present, in this paper, a spectral similarity measure that uses shape information of the spectral signatures. The intuition is to use the visual information in the shape of the spectral signatures. To this end, we consider the cost of transforming one spectrum shape to the another as a spectral distance between then. More the cost of transformation from the one shape to the another, greater is the distance between them. We use three alternative algorithms for measuring the cost of transformation: a) the first one is inspired by minimum edit distance algorithm, b) the second formulates the transformation as an assignment problem and solves it using Hungarian method, and c) the third one considers the angle and length transformation between the corresponding segments of the reference spectrum and the target spectrum. We compared our algorithms with other similarity measures such as Spectral Angle Mapper (SAM), Euclidean and so on. We find our methods to be more sensitive and better in discriminating the spectrally similar material as compared to SAM and Euclidean distances." @default.
- W4312405917 created "2023-01-04" @default.
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- W4312405917 date "2022-09-13" @default.
- W4312405917 modified "2023-09-24" @default.
- W4312405917 title "Shape Transformation Based Similarity Metric For Hyperspectral Data" @default.
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- W4312405917 doi "https://doi.org/10.1109/whispers56178.2022.9955048" @default.
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