In this paper, we present various bathymetric filters, based on the well-known matched filter (MF), adaptive MF, and adaptive cosine/coherence estimator detectors, for underwater target detection from hyperspectral remote-sensing data. In the case of unknown water characteristics, we also propose the GLRT-based bathymetric filter, which is a generalized likelihood ratio test-based filter that estimates these parameters and detects at the same time. The results of this estimation process, performed on both simulated and real data, are encouraging, since, under regular conditions of depth, water quality, and SNR, the accuracy is quite good. We show that these new detectors outperform the usual ones, obtained by detecting after correction of the water column effect by a classical method. We also show that the estimation errors do not greatly impact the detection performances, and therefore this underwater target detection method is self-sufficient and can be implemented without any a priori knowledge on the water column.