Underwater Target Detection With Hyperspectral Data: Solutions for Both Known and Unknown Water Quality
作者： Sylvain Jay , Mireille Guillaume , Jacques Blanc-Talon
DOI: 10.1109/JSTARS.2012.2185488
关键词: Underwater 、 Algorithm 、 Computer vision 、 Artificial intelligence 、 Filter (signal processing) 、 Object detection 、 Matched filter 、 Likelihood-ratio test 、 Hyperspectral imaging 、 Covariance matrix 、 Estimator 、 Computer science
摘要: In this paper, we present various bathymetric filters, based on the well-known matched filter (MF), adaptive MF, and cosine/coherence estimator detectors, for underwater target detection from hyperspectral remote-sensing data. case of unknown water characteristics, also propose GLRT-based filter, which is a generalized likelihood ratio test-based that estimates these parameters detects at same time. The results estimation process, performed both simulated real data, are encouraging, since, under regular conditions depth, quality, SNR, accuracy quite good. We show new detectors outperform usual ones, obtained by detecting after correction column effect classical method. errors do not greatly impact performances, therefore method self-sufficient can be implemented without any priori knowledge column.
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