An Antisubmarine Detection Method Using IR Spectrometer in Ocean Remote Sensing

2012 ◽  
Vol 490-495 ◽  
pp. 1337-1341
Author(s):  
Zhi Wu Ke ◽  
Rui Yu ◽  
Rui Xiang ◽  
Ke Long Zhang ◽  
Yong Ma
Keyword(s):  
Remote Sensing ◽  
Ir Spectrum ◽  
Detection Method ◽  
Sea Water ◽  
Atmospheric Correction ◽  
Correction Algorithm ◽  
Ir Imaging ◽  
Detection Approach ◽  
Infrared Spectrometer ◽  
Ocean Remote Sensing

According to the reduction of submarine noise level, Non-acoustics antisubmarine detection method becomes more important for the ocean remote sensing, especially infrared (IR) imaging remote sensing detection method. Conventional IR imaging remote sensing antisubmarine detection is more difficult because modern advanced submarine IR thermal radiance is not obvious. In this paper, our main purpose is to develop the advanced IR imaging remote sensing antisubmarine detection approach by using infrared spectrometer. The IR spectrum information derived from IR spectrometer in sea water and then retrieves the water-leaving spectra by the standard atmospheric correction algorithm. The submarine is detected by analyzing the water-leaving spectrum information. Results of comparisons with conventional IR imaging remote sensing antisubmarine detection, the modified approach is available to estimate the spectrum properties and effective to antisubmarine detection in sea water

scholarly journals CAAS: an atmospheric correction algorithm for the remote sensing of complex waters

2012 ◽  
Vol 30 (1) ◽  
pp. 203-220 ◽  
Author(s):  
P. Shanmugam
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Atmospheric Effects ◽  
Correction Algorithm ◽  
Ocean Colour ◽  
In Situ Data ◽  
Correction Scheme ◽  
The Impact

Abstract. The current SeaDAS atmospheric correction algorithm relies on the computation of optical properties of aerosols based on radiative transfer combined with a near-infrared (NIR) correction scheme (originally with assumptions of zero water-leaving radiance for the NIR bands) and several ancillary parameters to remove atmospheric effects in remote sensing of ocean colour. The failure of this algorithm over complex waters has been reported by many recent investigations, and can be attributed to the inadequate NIR correction and constraints for deriving aerosol optical properties whose characteristics are the most difficult to evaluate because they vary rapidly with time and space. The possibility that the aerosol and sun glint contributions can be derived in the whole spectrum of ocean colour solely from a knowledge of the total and Rayleigh-corrected radiances is developed in detail within the framework of a Complex water Atmospheric correction Algorithm Scheme (CAAS) that makes no use of ancillary parameters. The performance of the CAAS algorithm is demonstrated for MODIS/Aqua imageries of optically complex waters and yields physically realistic water-leaving radiance spectra that are not possible with the SeaDAS algorithm. A preliminary comparison with in-situ data for several regional waters (moderately complex to clear waters) shows encouraging results, with absolute errors of the CAAS algorithm closer to those of the SeaDAS algorithm. The impact of the atmospheric correction was also examined on chlorophyll retrievals with a Case 2 water bio-optical algorithm, and it was found that the CAAS algorithm outperformed the SeaDAS algorithm in terms of producing accurate pigment estimates and recovering areas previously flagged out by the later algorithm. These findings suggest that the CAAS algorithm can be used for applications focussing in quantitative assessments of the biological and biogeochemical properties in complex waters, and can easily be extended to other sensors such as OCM-2, MERIS and GOCI.

Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space

2000 ◽  
Vol 39 (6) ◽  
pp. 887 ◽  
Author(s):  
Bo-Cai Gao ◽  
Marcos J. Montes ◽  
Ziauddin Ahmad ◽  
Curtiss O. Davis
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
Hyperspectral Remote Sensing ◽  
Correction Algorithm

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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