Automated Underwater Object Recognition by Means of Fluorescence LIDAR

Year: 2015

Authors: Matteoli S., Corsini G., Diani M., Cecchi G., Toci G.

Autors Affiliation: Univ Pisa, Dept Informat Engn, I-56122 Pisa, Italy; Natl Res Council Italy, Inst Appl Phys Nello Carrara, I-50019 Sesto Fiorentino, Italy; Natl Res Council Italy, Natl Inst Opt, I-50125 Florence, Italy.

Abstract: This paper focuses on automated recognition of underwater objects by means of light detection and ranging (LIDAR) systems. Differently from most works involved in underwater object recognition with LIDAR, where objects are recognized by their shape, here the interest is distinguishing objects on the basis of physical/chemical properties of object materials. To this aim, laser-induced fluorescence (LIF) spectroscopy is exploited, and an ad hoc signal processing chain is presented to effectively analyze the LIF spectra extracted at the detected object-range. Specifically, the goal is that of automatically recognizing the detected object with respect to a database (DB) of objects of interest, which have been previously spectrally characterized by means of laboratory fluorescence measurements. To this aim, suitable physics-based methodologies are proposed to compensate the signal for water-column effects. A decision-theory-based framework is developed to approach spectral recognition of the detected object with respect to the object DB. Experimental results from a laboratory test-bed show that the proposed processing chain is effective at automatically recognizing objects submerged in an artificial water column at different depths, based on a diverse DB of sample materials. The presented approach is shown to provide great potential for automated object recognition in marine and other water environments.

Journal/Review: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

Volume: 53 (1)      Pages from: 375  to: 393

More Information: This work was supported by SEGREDIFESA of the Italian Ministry of Defense under PNRM project SULA (advanced Sensor for Underwater Laser 3-D Analysis and detection).
KeyWords: Automation; Chains; Decision theory; Fluorescence; Fluorescence spectroscopy; Materials properties; Optical radar; Signal processing, Automated object recognition; Automated recognition; Fluorescence measurements; Laser induced fluorescence spectroscopy; Light detection and ranging; Light detection and ranging systems; Object Detection; Water environments, Object recognition, automation; database; decision analysis; image analysis; lidar; signal processing; water column
DOI: 10.1109/TGRS.2014.2322676

ImpactFactor: 3.360
Citations: 17
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