Underwater target detection with hyperspectral remote-sensing imagery

This paper presents a new way of detecting underwater targets with hyperspectral remote-sensing data. The idea is to use a bathymetric model of subsurface reflectance to correct the spectral distortions due to water crossing. Then we derive the Matched filter (MF) from the Likelihood Ratio Test (LRT) built to decide whether the target is present or absent. Tested on both simulated and real images, this new detector appears to overcome classical filters in case of underwater targets. If the depth is unknown, it can be estimated using the maximum likelihood approach, and we show on simulations that detection performances are not very sensitive to the depth estimation accuracy.

[1]  J. Pulliainen,et al.  Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons. , 2001, The Science of the total environment.

[2]  Dar A. Roberts,et al.  A forward image model for passive optical remote sensing of river bathymetry , 2009 .

[3]  Luis O. Jimenez-Rodriguez,et al.  Clutter modeling for subsurface detection in hyperspectral imagery using Markov random fields , 2004, SPIE Optics + Photonics.

[4]  C. Mobley,et al.  Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. , 1999, Applied optics.

[5]  Alexander Berk,et al.  Remote bathymetry of the littoral zone from AVIRIS, LASH, and QuickBird imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Chris Roelfsema,et al.  A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data , 2009 .

[7]  Luis O. Jimenez-Rodriguez,et al.  Subsurface detection of coral reefs in shallow waters using hyperspectral data , 2003, SPIE Defense + Commercial Sensing.

[8]  Georg Martin,et al.  Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters—a Baltic Sea case study , 2006 .

[9]  E. LeDrew,et al.  Remote sensing of aquatic coastal ecosystem processes , 2006 .

[10]  André Morel,et al.  Diffuse reflectance of oceanic shallow waters: influence of water depth and bottom albedo , 1994 .

[11]  E. LeDrew,et al.  Remote sensing of aquatic coastal ecosystem processes : science and management applications , 2006 .

[12]  H. Claustre,et al.  Variability in the chlorophyll‐specific absorption coefficients of natural phytoplankton: Analysis and parameterization , 1995 .

[13]  Wilson Rivera,et al.  Sensitivity Analysis of a Hyperspectral Inversion Model for Remote Sensing of Shallow Coastal Ecosystems , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[14]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..