&lt;title&gt;Target detection in desert backgrounds: infrared hyperspectral measurements and analysis&lt;/title&gt;

Eismann, Michael T.; Seldin, John H.; Schwartz, Craig R.; Maxwell, James R.; Ellis, Kenneth K.; Cederquist, Jack N.; Stocker, Alan D.; Oshagan, Ara; Johnson, Ray O.; Shaffer, William A.; Surette, Marc R.; McHugh, M. J.; Schaum, A.; Stotts, Larry B.

doi:10.1117/12.217672

Cited by 8 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to ensure the target detection ability of the satellite-borne infrared hyperspectral sensor, efficient optimization of the imaging index is required based on the evaluation for target detection performance during integrative system design period. Michael T. Eismann used multidimensional signal-to-clutter ratio (SCR) for the first time to evaluate the detection ability of infrared hyperspectral sensor [1] during thermal crossover period [2] , and analyzed imaging index requirement of target detection from aircraft or tower [3,4] . However, the effects of atmosphere scattering and absorbing on signal radiated or reflected from objects under the condition of remote sensing was not considered, which could hardly guide the integrative design of satelliteborne infrared hyperspectral sensor.…”

Section: Introductionmentioning

confidence: 99%

Optimized integrative system design in satellite with MWIR/LWIR hyperspectral imaging based on multidimensional SCR and random forest learning

Liu,

Li,

Cao

et al. 2023

Target and Background Signatures IX

View full text Add to dashboard Cite

At present, aerospace development puts forward an urgent need for the integrative system design in satellite with MWIR/LWIR hyperspectral imaging spectrometer to provide the solution of target detection problem under the circumstance of weak thermal contrast between target and background at night, which can hardly be solved by traditional thermal infrared imaging system. In order to efficiently optimize the imaging index of the MWIR/LWIR hyperspectral imaging spectrometer, i.e. ground sample distance (GSD), spectral resolution, noise equivalent temperature difference (NETD), this paper proposed a novel optimized integrative system design method based on evaluation for target detection performance through multidimensional signal-to-clutter ratio (SCR). For assumed Gaussian target and background statistics, multidimensional SCR is the primary parameter describing the detection performance of a variety of detection algorithms based on the generalized maximum likelihood formulation, especially when the thermal contrast between target and background approach to zero. Therefore, we calculate the multidimensional SCR from MWIR/ LWIR hyperspectral images that are obtained through the simulation of satellite borne hyperspectral imaging chain with imaging indices, as the equivalent of detection performance. Based on the training datasets composed of multidimensional SCR and imaging indices, we can use random forest regression to identify the sensitivities of different imaging indices to multidimensional SCR. The sensitivity analysis of multidimensional SCR can help to determine the key to index optimization, guiding the integrative system design. More importantly, the relationship between the SCR and imaging indices can be predicted through random forest learning, which can be applied to the further global optimization of imaging indices with related optimization algorithms. With our proposed method, the integrative system design is closely associated to the demand for target detection task, meeting the satellite-borne detection performance requirements, and the manufacturing cost could be reduced due to the absence of excessive index optimization.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimized integrative system design in satellite with MWIR/LWIR hyperspectral imaging based on multidimensional SCR and random forest learning

Liu,

Li,

Cao

et al. 2023

Target and Background Signatures IX

View full text Add to dashboard Cite

show abstract

“…Hyperspectral imagery shows great potential for this task because it provides both spatial and spectral features about the targets and backgrounds in the imagery. There is reason to believe from published studies, [4]- [6], that the spectral characteristics of natural clutter differ in significant ways from the spectral characteristics of man-made objects, thus hyperspectral sensors can be a tremendous aid in discriminating between the two classes.…”

mentioning

confidence: 99%

Hyperspectral imagery: clutter adaptation in anomaly detection

Schweizer

Moura

2000

IEEE Trans. Inform. Theory

118

View full text Add to dashboard Cite

Abstract-Hyperspectral sensors are passive sensors that simultaneously record images for hundreds of contiguous and narrowly spaced regions of the electromagnetic spectrum. Each image corresponds to the same ground scene, thus creating a cube of images that contain both spatial and spectral information about the objects and backgrounds in the scene. In this paper, we present an adaptive anomaly detector designed assuming that the background clutter in the hyperspectral imagery is a three-dimensional Gauss-Markov random field. This model leads to an efficient and effective algorithm for discriminating man-made objects (the anomalies) in real hyperspectral imagery. The major focus of the paper is on the adaptive stage of the detector, i.e., the estimation of the Gauss-Markov random field parameters. We develop three methods: maximum-likelihood; least squares; and approximate maximum-likelihood. We study these approaches along three directions: estimation error performance, computational cost, and detection performance. In terms of estimation error, we derive the Cramér-Rao bounds and carry out Monte Carlo simulation studies that show that the three estimation procedures have similar performance when the fields are highly correlated, as is often the case with real hyperspectral imagery. The approximate maximum-likelihood method has a clear advantage from the computational point of view. Finally, we test extensively with real hyperspectral imagery the adaptive anomaly detector incorporating either the least squares or the approximate maximum-likelihood estimators. Its performance compares very favorably with that of the RX algorithm, an alternative detector commonly used with multispectral data, while reducing by up to an order of magnitude the associated computational cost.Index Terms-Anomaly detection, Cramér-Rao bounds, GaussMarkov random field, hyperspectral imagery, least squares, maximum likelihood, multispectral imagery, ultraspectral imagery.

show abstract