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18 September 2018 Spectral vision system for discriminating small pelagic species caught by small-scale fishing
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Abstract
The management of fish stocks in Chile caught by small-scale fishing boats are subject to catch quotas. Due to the massive number of fish landings, solely a very small number of landings can be inspected. In this paper, we present the first step in order to develop a vision system for automatically checking the fish quotas. This first step consists in automatically classifying the different fish species that must be checked, based upon the hypothesis that different small pelagic fish species should have different spectral signatures. Thus, we collected hyperspectral cubes, in the Near Infrared (NIR) band, for the following three species of interest: Chilean Silverside (Odontesthes regia), Southern Rays Bream (Brama australis), and Silver Hake (Merlucciidae). The hypercubes, containing 256 spectral bands in the range of 900-1700 nm, were processed and labeled to obtain the spectral signatures of the species. The spectral signatures were used to develop k-nearest neighbor and support vector machine classifiers. Their performance was compared using n-fold cross-validation and 5000 trials. When only a small subset of spectral bands was used by the classifiers, the average classification rate achieved was approximately 80%. When the entire spatial-spectral information was used, the average classification rate raised to 90%.
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Diego Ramírez and Jorge E. Pezoa "Spectral vision system for discriminating small pelagic species caught by small-scale fishing", Proc. SPIE 10766, Infrared Sensors, Devices, and Applications VIII, 107660V (18 September 2018); https://doi.org/10.1117/12.2325457
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