Non-Gaussian CFAR techniques for target detection in high resolution SAR images

S. Kuttikkad; R. Chellappa

doi:10.1109/ICIP.1994.413444

Non-Gaussian CFAR techniques for target detection in high resolution SAR images

S. Kuttikkad
, R. Chellappa

Research output: Contribution to journal › Conference article › peer-review

Abstract

Constant False Alarm Rate (CFAR) processing of Synthetic Aperture Radar (SAR) images facilitates target detection in spatially varying background clutter. The traditional Rayleigh distribution does not appear to be a good choice for modeling the natural terrain backscatter in high resolution SAR. We use the Weibull and K distributions to model clutter since they seem to fit observed data better and also include the Rayleigh distribution as a special case. The Cell Averaged CFAR technique works well in situations where a single, small target is present in locally homogeneous clutter. The Order Statistic CFAR is more useful for larger targets and in multiple target situations. Comparisons are made between the various CFAR techniques by applying them to real, high-resolution SAR images, obtained from the MIT Lincoln Laboratory.

Original language	English (US)
Article number	413444
Pages (from-to)	910-914
Number of pages	5
Journal	Proceedings - International Conference on Image Processing, ICIP
Volume	1
DOIs	https://doi.org/10.1109/ICIP.1994.413444
State	Published - 1994
Externally published	Yes
Event	The 1994 1st IEEE International Conference on Image Processing - Austin, TX, USA Duration: Nov 13 1994 → Nov 16 1994

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.1994.413444

Cite this

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title = "Non-Gaussian CFAR techniques for target detection in high resolution SAR images",

abstract = "Constant False Alarm Rate (CFAR) processing of Synthetic Aperture Radar (SAR) images facilitates target detection in spatially varying background clutter. The traditional Rayleigh distribution does not appear to be a good choice for modeling the natural terrain backscatter in high resolution SAR. We use the Weibull and K distributions to model clutter since they seem to fit observed data better and also include the Rayleigh distribution as a special case. The Cell Averaged CFAR technique works well in situations where a single, small target is present in locally homogeneous clutter. The Order Statistic CFAR is more useful for larger targets and in multiple target situations. Comparisons are made between the various CFAR techniques by applying them to real, high-resolution SAR images, obtained from the MIT Lincoln Laboratory.",

author = "S. Kuttikkad and R. Chellappa",

note = "Publisher Copyright: {\textcopyright} 1994 IEEE.; The 1994 1st IEEE International Conference on Image Processing ; Conference date: 13-11-1994 Through 16-11-1994",

year = "1994",

doi = "10.1109/ICIP.1994.413444",

language = "English (US)",

volume = "1",

pages = "910--914",

journal = "Proceedings - International Conference on Image Processing, ICIP",

issn = "1522-4880",

publisher = "IEEE Computer Society",

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T1 - Non-Gaussian CFAR techniques for target detection in high resolution SAR images

AU - Kuttikkad, S.

AU - Chellappa, R.

PY - 1994

Y1 - 1994

N2 - Constant False Alarm Rate (CFAR) processing of Synthetic Aperture Radar (SAR) images facilitates target detection in spatially varying background clutter. The traditional Rayleigh distribution does not appear to be a good choice for modeling the natural terrain backscatter in high resolution SAR. We use the Weibull and K distributions to model clutter since they seem to fit observed data better and also include the Rayleigh distribution as a special case. The Cell Averaged CFAR technique works well in situations where a single, small target is present in locally homogeneous clutter. The Order Statistic CFAR is more useful for larger targets and in multiple target situations. Comparisons are made between the various CFAR techniques by applying them to real, high-resolution SAR images, obtained from the MIT Lincoln Laboratory.

AB - Constant False Alarm Rate (CFAR) processing of Synthetic Aperture Radar (SAR) images facilitates target detection in spatially varying background clutter. The traditional Rayleigh distribution does not appear to be a good choice for modeling the natural terrain backscatter in high resolution SAR. We use the Weibull and K distributions to model clutter since they seem to fit observed data better and also include the Rayleigh distribution as a special case. The Cell Averaged CFAR technique works well in situations where a single, small target is present in locally homogeneous clutter. The Order Statistic CFAR is more useful for larger targets and in multiple target situations. Comparisons are made between the various CFAR techniques by applying them to real, high-resolution SAR images, obtained from the MIT Lincoln Laboratory.

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UR - https://www.scopus.com/pages/publications/84996841762#tab=citedBy

U2 - 10.1109/ICIP.1994.413444

DO - 10.1109/ICIP.1994.413444

M3 - Conference article

AN - SCOPUS:84996841762

SN - 1522-4880

VL - 1

SP - 910

EP - 914

JO - Proceedings - International Conference on Image Processing, ICIP

JF - Proceedings - International Conference on Image Processing, ICIP

M1 - 413444

T2 - The 1994 1st IEEE International Conference on Image Processing

Y2 - 13 November 1994 through 16 November 1994

ER -

Non-Gaussian CFAR techniques for target detection in high resolution SAR images

Abstract

ASJC Scopus subject areas

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