In this paper, we present various bathymetric filters, based on the well-known MF, AMF and ACE detectors, for underwater target detection from hyperspectral remote-sensing data. In the case of unknown water characteristics, we also propose the GBF, a GLRT-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 impact much 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.