Study on Target Detection of SAR Image Using Improved Fractal Feature

Cheng De-bao; Hu Feng-ming; Yang Ru-liang

doi:10.3724/SP.J.1146.2008.00416

Volume 31 Issue 1

Dec. 2010

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Article Navigation > Journal of Electronics & Information Technology > 2009 > 31(1): 164-168

Cheng De-bao, Hu Feng-ming, Yang Ru-liang. Study on Target Detection of SAR Image Using Improved Fractal Feature[J]. Journal of Electronics & Information Technology, 2009, 31(1): 164-168. doi: 10.3724/SP.J.1146.2008.00416

Citation:

Cheng De-bao, Hu Feng-ming, Yang Ru-liang. Study on Target Detection of SAR Image Using Improved Fractal Feature[J]. Journal of Electronics & Information Technology, 2009, 31(1): 164-168. doi: 10.3724/SP.J.1146.2008.00416

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Study on Target Detection of SAR Image Using Improved Fractal Feature

doi: 10.3724/SP.J.1146.2008.00416

Received Date: 2008-04-13
Rev Recd Date: 2008-07-30
Publish Date: 2009-01-19

Abstract

Abstract

The improved fractal feature of each pixel can be extracted based on the filtered image using the exponential wavelet at one scale and energy functions. This paper studies the method of the improved fractal feature for SAR images target detection. The results of target detections using the improved fractal feature are compared with that using the method of Extended Fractal (EF) for SAR images in the simple and complex backgrounds respectively. Results state the method using the improved fractal feature can not only detect all size-fixed targets but also have lower false alarm rates, spatial resolution of the detected targets are higher and the locations of the detected targets are more accurate in both of the backgrounds, but worse false alarm rates in complex background than in the simple background using the improved fractal feature.

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References

Berizzi F, Bertini G, and Martorella M, et al.. Twodimensionalvariation algorithm for fractal analysis of seaSAR images[J].IEEE Trans. on Geoscience and Remote Sensing.2006, 44(9):2361-2373[2]Martino G D, Iodice A, and Riccio D, et al.. A novel approachfor disaster monitoring: fractal models and tools[J].IEEE Trans.on Geoscience and Remote Sensing.2007, 45(6):1559-1570[3]Charalampidis D and Kasparis T. Wavelet-based rotationalinvariant roughness features for texture classification andsegmentation[J].IEEE Trans. on Image Processing.2002, 11(8):825-837[4] https://www.sdms.afrl.af.mil/request/data_request.php.[5]Kaplan L M, Murenzi R, and Namuduri K. Extended fractalfeature for first stage SAR target detection[J].SPIE.1999,Vol.3721:35-46[6]Kaplan L M. Improved SAR target detection via extendedfractal features[J].IEEE Trans. on Aerospace and ElectronicSystems.2001, 37(2):436-451[7]Charalampidis D and Stein G W. Target detection based onmultiresolution fractal analysis. SPIE proceedings, SignalProcessing, Sensor Fusion, and Target Recognition XVI,April 2007, Vol.6567: 65671B-1-65671B-8.[8]Subotic N S, Thelen B J, and Gorman J D, et al..Multiresolution detection of coherent radar targets[J].IEEETrans. on Image Processing.1997, 6(1):21-35[9]Salazar J S and Hush D R. Statistical modeling of targets andclutter in single-look non-polarimetric SAR imagery.Proc.EASTED Inter.Conf.on Signal and Image Processing,Las Vegas, NV(United States), 1998: 272-276.

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