Image metrics for clutter characterization

Namuduri, Kamesh; Bouyoucef, Karim; Kaplan, Lance

doi:10.1109/icip.2000.899452

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Other algorithms are based primarily on the variability in colors, hues, luminance, and other image features within a given space (e.g., Fadiran et al, 2006;Jansen & van Kreveld, 1998;Kim et al, 2011;Meitzler, Gerhart, & Singh, 1998;Namuduri, Bouyoucef, & Kaplan, 2000;Shirvaikar & Trivedi, 1992). One well-known such method is the Schmieder-Weathersby statistical variance (SV) clutter metric, which consists of dividing an image into grid cells with an area twice that of the target and then taking the root mean squared (RMS) grayscale variance between these different grid cells (Schmieder & Weathersby, 1983).…”

Section: The Display-density Perspective Of Cluttermentioning

confidence: 99%

Display Clutter

Moacdieh

Sarter

2014

Hum Factors

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Section: The Display-density Perspective Of Cluttermentioning

confidence: 99%

Display Clutter

Moacdieh

Sarter

2014

Hum Factors

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“…Image complexity metrics for automatic target recognizers [1] and clutter characterization [2,3] have been studied previously to quantify the complexity of hyperspectral images and its correlation to target recognition accuracy. In this work, image features such as average histogram entropy, signal to noise ratio, edge detection, among others, have been used to quantify the complexity of hyperspectral images and study the behavior of the classifiers and unmixing algorithms of the HIAT.…”

Section: Image Complexity Analysismentioning

confidence: 99%

Comparative study of state-of-the-art algorithms for hyperspectral image analysis

Rivera-Borrero

Hunt

2007

SPIE Proceedings

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This work studies the end-to-end performance of hyperspectral classification and unmixing systems. Specifically, it compares widely used current state of the art algorithms with those developed at the University of Puerto Rico. These include algorithms for image enhancement, band subset selection, feature extraction, supervised and unsupervised classification, and constrained and unconstrained abundance estimation. The end to end performance for different combinations of algorithms is evaluated. The classification algorithms are compared in terms of percent correct classification. This method, however, cannot be applied to abundance estimation, as the binary evaluation used for supervised and unsupervised classification is not directly applicable to unmixing performance analysis. A procedure to evaluate unmixing performance is described in this paper and tested using coregistered data acquired by various sensors at different spatial resolutions. Performance results are generally specific to the image used. In an effort to try and generalize the results, a formal description of the complexity of the images used for the evaluations is required. Techniques for image complexity analysis currently available for automatic target recognizers are included and adapted to quantify the performance of the classifiers for different image classes.

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“…Some of which are extenRecognition (ATR) algorithm as the desired target. The quansions of metrics used in the single-band clutter characterizatity, locations and nature of these objects will determine the tion schemes in [1] and [5]. The clutter metrics that we emclutter level in the image.…”

Section: Introductionmentioning

confidence: 99%

“…This will result in a reduction in the dimensions of the clutter metrics space and thus a reduction in the required number of operations to comMetrics Derivedfrom Single-bands The image clutter metpute them. The aim is to reduce the dimensionality yet retain rics that were used in [1] and [5] were mostly based on stasignificant information about clutter in the images in the cluttistical features of the images. We implemented these and ter metrics space.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Approach to Quantifying Clutter in Hyperspectral Infrared Images

Fadiran

Molnár²,

Kaplan³

2006 IEEE Aerospace Conference

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A method to quantify clutter in hyperspectral intive basis, frared (HSI) images in a framework similar to work done . the need for a measure to form the basis for a preon single-band images is presented. Hereby, all objects in a processing step to discard images, or make a decision on furscene that may be mistaken for targets by an Automatic Target ther processing, Recognition (ATR) algorithm are considered clutter. A hy-. the same need stated above for a post-processing step to perspectral image contains a number of contiguous discrete determine the reliability of the result of running an ATR on a bands within the spectrum. The aim is to obtain a measure scene, and of complexity for hyperspectral images, which will indicate . the inverse-problem problem of creating clutter on ground the inherent difficulty for an ATR to detect targets. We imscenes. e.g. camouflaging. plemented 129 different image clutter metrics, and computed them for a database of synthesized HSI images. A matched Since it is difficult to capture the multifaceted nature of imfilter ATR was used to determine the amount of clutter in the age clutter in a single number, our aim is to obtain bounds images as a baseline. We developed a method to select a subon the performance of any ATR on a scene based on a genset of the metrics that in combination correlated best with the eral clutter quantification scheme. That is, a high value of amount of clutter in an image, and defined this as the clutter this quantity will indicate that any ATR will produce a high complexity measure (CCM). Multiple runs of this selection false alarm (FA) rate. A low value may, however, not result procedure for different training image groups show a domiin a low FA rate. This will depend on the exact nature of the nance of a further subset ofmetrics that best predict the CCM.ATR. Several attempts have been made to derive such a clutOur results also show that the CCM obtained from a varying ter metric for single band images [1]- [4]. In previous work, number ofrandom sample images generalizes well for the enwe obtained and successfully used a single-band clutter comtire database. plexity measure as a basis for band selection in a multi-band ATR detection scheme in [5].

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Image metrics for clutter characterization

Cited by 4 publications

References 6 publications

Display Clutter

Display Clutter

Comparative study of state-of-the-art algorithms for hyperspectral image analysis

A Statistical Approach to Quantifying Clutter in Hyperspectral Infrared Images

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