&lt;title&gt;Detection of targets in terrain clutter by using multispectral infrared image processing&lt;/title&gt;

Hoff, Lawrence E.; Evans, John R.; Bunney, Laura E.

doi:10.1117/12.45679

Cited by 11 publications

(8 citation statements)

References 3 publications

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“…Tran=exp(-axCL) (2) The signal in the pixel is then the sum of the transmitted (and attenuated) background radiance plus the self radiance of the chemical vapor (calculated from Beer's Law):…”

Section: Detection Mechanism and Algorithm Descriptionmentioning

confidence: 99%

<title>Detection and identification of chemical vapors using multispectral infrared</title>

Hoff

Winter

1994

Substance Identification Analytics

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The process of manufacture of certain products makes use of a variety of volatile organic chemicals. These chemicals, despite recovery procedures, are released into the environment as trace gases, which can be detected as an indicator of the manulactunng process. The detection of these chemical vapors can be accomplished by several techniques ranging from direct sampling of the atmosphere to spectroscopy. It is an adaptation of the spectroscopy technique that is addressed in this paper since it is more applicable to remote surveillance. Spectrometers detect the presence of chemical vapors by the presence in the long wave portion of the infrared spectrum of telltale absorption features characteristic of one or more of the chemicals used in the manufacturing process. While pointable spectrometers, such as a Fourier Transform Spectrometer (Fl'S), have been demonstrated in field tests, the instruments can not easily be used for airborne surveillance when the location of the source is unknown. In this paper an approach using multi-spectral infrared is discussed. Multi-spectral infrared, coupled with advanced image processing procedures, offers the possibility of wide area surveillance and the detection of low concentrations of chemical vapors. A field experiment was performed using the ARPA Multi-Spectral Infrared Camera (MUSIC) sensor mounted on an aircraft. The sensor aircraft flew over a controlled diethyl ether release in a tropical rain forest acquiring image data from both 10000 ft and 22000 ft altitude. The data was processed using multi-spectral algorithms and the vapor was detected over both an open area and the rain forest canopy. This detection was made possible by the removal of most of the background scene by multi-spectral processing. DETECTION MECHANISM AND ALGORITHM DESCRIPTIONMulti-spectral processing relies on the acquisition of two or more images with one image taken in a spectral band centered on the chemical absorption feature and a second image taken in a band well off the absorption feature. If the background features of the images are well correlated which is likely in images of vegetation in the 8 to 14 im region of the spectrum, high levels of processing gain can be achieved. The theory of multi-spectral detection processing is discussed first. The 1ev-el of the gain due to spectral processing, along with the additional gain due to spatial and temporal processing will be presented'.Multi-spectral infrared processing is an application of multi-channel processing, which has been demonstrated to have application in such diverse fields as side-lobe canceling radar and optics. There are two principles involved in the basic concept of multi-spectral infrared processing. The first is that the infrared images of natural background in different thermally dominated infrared bands are highly correlated. This high level of correlation allows the background data collected in one band to be used to cancel the clutter (natural background) in the other. Many measurements of natural backgrounds, especially ve...

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Section: Detection Mechanism and Algorithm Descriptionmentioning

confidence: 99%

<title>Detection and identification of chemical vapors using multispectral infrared</title>

Hoff

Winter

1994

Substance Identification Analytics

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“…But the performance highly depends on SNR. Hoff etc [4] use 2D LMS adaptive scheme to distinguish between targets and clutter. According to infrared IT{ II\Ii IT{Fl) multi-spectral characteristic information of targets, background and decoys, Wu etc [6] present an automatic target recognition algorithm by fusing target information of space and spectrum.…”

Section: ． Introductionmentioning

confidence: 99%

A new small and dim targets detection and recognition algorithm based on infrared dual bands imaging system

et al. 2011

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Abstract：In this paper, infrared radiation characteristics in different bands of flight targets, background and decoys are analyzed. According to the analytical results, a new small and dim targets detection and recognition algorithm based on infrared dual bands is present. Infrared dual bands imaging system includes infrared mid-wavelength imaging system and infrared long-wavelength imaging system. Images from these two imaging system are registered in time and space firstly. After background suppression, then the small and dim targets are detected from MW infrared images and LW infrared images respectively. At last, by using spatial correlation and spectral correlation of targets, interferes and decoys are eliminated from target candidates. The genuine targets are separated and recognized. The algorithm is applied to simulation dual bands images and real infrared dual band images. The experimental results show that the algorithm present in this paper is effective. Keywords: infrared small and dim targets, infrared dual-band image, target separation and recognition 1． IntroductionInfrared Search and Track(IRST) system has advantages of high resolution, invisibility, fine anti-jamming ability, small size, lightweight and high maneuverability. So IRST system is an important part of modern information confrontation system, and obtains more attention in recent years. In order to meet the increasing demand of confrontation system, IRST system is required to detect long distance targets to ensure enough pre-alarm time. But at this moment the targets are small and dim in infrared images. For complex environment, there exist many noises from environment and decoys etc. Hence it is difficult to separate targets from complex background because of its small size, low gray, usually occupying only several pixels, no shape and texture information and so on. Therefore, how small and dim targets can be reliably detected in complex background has become a hot research issue and has important theoretical and practical value. Infrared imaging system of single band is susceptible to be interfered by clutter and decoys which result in performance degradation. However, multi-spectral imaging system can make full use of radiation characteristics of different wavelengths of targets, clutter and decoys to extract genuine targets.According to different radiation characteristics of missile plume and interference, Bretier etc[1] use the ratio of dual color in middle infrared band to identify targets. And Yu etc[2] apply maximum likelihood ratio approach to automatic targets recognition. But the performance highly depends on SNR. Hoff etc[4] use 2D LMS adaptive scheme to distinguish between targets and clutter. According to infrared Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/27/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx IT{ II\Ii IT{Fl)multi-spectral characteristic information of targets, background and decoys, Wu etc[6] present an automatic target recognition algorithm by fusing target inform...

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“…The difference of spectral radiation and reflection characteristics in diverse wavebands between background and real target can be generalized as [1][2][3][4] : 1) the radiation of natural background and some targets can be approximated by greybody radiation, and the target's multispectral image in adjacent sub-band is highly correlated; 2) The radiation of decoy is selective, the distribution of radiant intensity in different sub-band is distinctive and correlation is low [5] . Hence, an automatic target recognition algorithm based on statistical dispersion of infrared multispectral images(SDOIMI) is proposed here.…”

Section: Introductionmentioning

confidence: 99%

Automatic target recognition algorithm based on statistical dispersion of infrared multispectral image

Zhang

Cao²,

Wu³

et al. 2009

SPIE Proceedings

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A novel automatic target recognition algorithm based on statistical dispersion of infrared multispectral images(SDOIMI) is proposed. Firstly, infrared multispectral characteristic matrix of the scenario is constructed based on infrared multispectral characteristic information (such as radiation intensity and spectral distribution etc.) of targets, background and decoys. Then the infrared multispectral characteristic matrix of targets is reconstructed after segmenting image by maximum distance method and fusing spatial and spectral information. Finally, an statistical dispersion of infrared multispectral images(SDOIMI) recognition criteria is formulated in terms of spectral radiation difference of interesting targets. In simulation, nine sub-bands multispectral images of real ship target and shipborne aerosol infrared decoy modulated by laser simulating ship geometry appearance are obtained via using spectral radiation curves. Digital simulation experiment result verifies that the algorithm is effective and feasible.

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<title>Detection of targets in terrain clutter by using multispectral infrared image processing</title>

Cited by 11 publications

References 3 publications

<title>Detection and identification of chemical vapors using multispectral infrared</title>

<title>Detection and identification of chemical vapors using multispectral infrared</title>

A new small and dim targets detection and recognition algorithm based on infrared dual bands imaging system

Automatic target recognition algorithm based on statistical dispersion of infrared multispectral image

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