SAR target classification using sparse representations and spatial pyramids

Peter Knee, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations


We consider the problem of automatically classifying targets in synthetic aperture radar (SAR) imagery using image partitioning and sparse representation based feature vector generation. Specifically, we extend the spatial pyramid approach, in which the image is partitioned into increasingly fine sub-regions, by using a sparse representation to describe the local features in each sub-region. These feature descriptors are generated by identifying those dictionary elements, created via k-means clustering, that best approximate the local features for each sub-region. By systematically combining the results at each pyramid level, classification ability is facilitated by approximate geometric matching. Results using a linear SVM for classification along with SIFT, FFT-magnitude and DCT-based local feature descriptors indicate that the use of a single element from the dictionary to describe the local features is sufficient for accurate target classification. Continuing work both in feature extraction and classification will be discussed, with emphasis placed on the need for classification amid heavy target occlusion.

Original languageEnglish (US)
Title of host publicationRadarCon'11 - In the Eye of the Storm
Subtitle of host publication2011 IEEE Radar Conference
Number of pages5
StatePublished - 2011
Event2011 IEEE Radar Conference: In the Eye of the Storm, RadarCon'11 - Kansas City, MO, United States
Duration: May 23 2011May 27 2011

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Other2011 IEEE Radar Conference: In the Eye of the Storm, RadarCon'11
Country/TerritoryUnited States
CityKansas City, MO

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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