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- W2765788435 abstract "In this paper, we test anomaly change detection algorithms in hyperspectral images. Focusing on difference-based algorithms, our goal is to optimize performance using new methods that utilize the spatial and statistical characteristics of the images. These methods increase the probability of detection while minimizing false alarms. The algorithms are tested on the hyperspectral images of the Rochester Institute of Technology (RIT)." @default.
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- W2765788435 date "2016-08-01" @default.
- W2765788435 modified "2023-09-25" @default.
- W2765788435 title "Analysis of hyperspectral anomaly change detection algorithms" @default.
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- W2765788435 doi "https://doi.org/10.1109/whispers.2016.8071746" @default.
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