Hyperspectral target detection using Gaussian filter and post-processing

Alam, Mohammad S.; Islam, Nazrul; Bal, Abdullah; Karim, Mohammad A.

doi:10.1016/j.optlaseng.2008.05.019

Cited by 26 publications

(16 citation statements)

References 12 publications

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“…As a second baseline method, the entire spectrum curve, where all the bands are selected, is used [13]. These conventional methods are compared with the proposed band selection methods, such as band selection by spectral analysis (Proposed 1) and by spectral + spatial analysis (Proposed 2).…”

Section: Resultsmentioning

confidence: 99%

Unsupervised Spectral‐Spatial Feature Selection‐Based Camouflaged Object Detection Using VNIR Hyperspectral Camera

Kim

2015

The Scientific World Journal

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The detection of camouflaged objects is important for industrial inspection, medical diagnoses, and military applications. Conventional supervised learning methods for hyperspectral images can be a feasible solution. Such approaches, however, require a priori information of a camouflaged object and background. This letter proposes a fully autonomous feature selection and camouflaged object detection method based on the online analysis of spectral and spatial features. The statistical distance metric can generate candidate feature bands and further analysis of the entropy-based spatial grouping property can trim the useless feature bands. Camouflaged objects can be detected better with less computational complexity by optical spectral-spatial feature analysis.

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Section: Resultsmentioning

confidence: 99%

Unsupervised Spectral‐Spatial Feature Selection‐Based Camouflaged Object Detection Using VNIR Hyperspectral Camera

Kim

2015

The Scientific World Journal

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“…As a second baseline method, the entire spectrum curve, where all the bands are selected, is used. 21 These conventional methods are compared with the proposed band selection methods, such as band selection by spectral analysis (Proposed 1) and by fusion of spectral and spatial analysis (Proposed 2). The detection rate (DR), false alarm rate (FAR), and the number of bands used for quantitative comparison are used.…”

Section: Resultsmentioning

confidence: 99%

Cooperative spectral and spatial feature fusion for camouflaged target detection

Kim

Shim

2015

SPIE Proceedings

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This paper presents a novel camouflaged target detection method using spectral and spatial feature fusion. Conventional unsupervised learning methods using spectral information only can be feasible solutions. Such approaches, however, sometimes produce incorrect detection results because spatial information is not considered. This paper proposes a novel band feature selection method by considering both the spectral distance and spatial statistics after spectral normalization for illumination invariance. The statistical distance metric can generate candidate feature bands and further analysis of the spatial grouping property can trim the useless feature bands. Camouflaged targets can be detected better with less computational complexity by the spectral-spatial feature fusion.

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“…For this purpose, the measured hyperspectral data is compared with the reflectance spectra (spectral signature) of the material derived from field work or laboratory study to determine whether the given input scene contains a target [3][4]. If we have no a priori spectral information about targets, the detection approach is based on searching for pixels whose spectral content is significantly different from the spectral content of the local background [5].…”

Section: Introductionmentioning

confidence: 99%

“…Detection algorithms based on statistical analysis emerged for such an environment by maximizing the probability of detection and minimizing the probability of false alarm. Although various detection algorithms using statistical information about targets and background classes have been developed over the years, insufficient training data and high dimensionality of spectra reduce the performance and effectiveness of these detectors [4].…”

Section: Introductionmentioning

confidence: 99%

Vector tunnel algorithm for hyperspectral target detection

2014

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In this study, targets and nontargets in a hyperspectral image are characterized in terms of their spectral features. Target detection problem is considered as a two-class classification problem. For this purpose, a vector tunnel algorithm (VTA) is proposed. The vector tunnel is characterized only by the target class information. Then, this method is compared with Euclidean Distance (ED), Spectral Angle Map (SAM) and Support Vector Machine (SVM) algorithms. To obtain the training data belonging to target class, the training regions are selected randomly. After determination of the parameters of the algorithms with the training set, detection procedures are accomplished at each pixel as target or background. Consequently, detection results are displayed as thematic maps.The algorithms are trained with the same training sets, and their comparative performances are tested under various cases. During these studies, various levels of thresholds are evaluated based on the efficiency of the algorithms by means of Receiver Operating Characteristic Curves (ROC) as well as visually.

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Hyperspectral target detection using Gaussian filter and post-processing

Cited by 26 publications

References 12 publications

Unsupervised Spectral‐Spatial Feature Selection‐Based Camouflaged Object Detection Using VNIR Hyperspectral Camera

Unsupervised Spectral‐Spatial Feature Selection‐Based Camouflaged Object Detection Using VNIR Hyperspectral Camera

Cooperative spectral and spatial feature fusion for camouflaged target detection

Vector tunnel algorithm for hyperspectral target detection

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