TY - GEN
T1 - SAR target classification using sparse representations and spatial pyramids
AU - Knee, Peter
AU - Thiagarajan, Jayaraman J.
AU - Ramamurthy, Karthikeyan Natesan
AU - Spanias, Andreas
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80052454508&partnerID=8YFLogxK
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U2 - 10.1109/RADAR.2011.5960546
DO - 10.1109/RADAR.2011.5960546
M3 - Conference contribution
AN - SCOPUS:80052454508
SN - 9781424489022
T3 - IEEE National Radar Conference - Proceedings
SP - 294
EP - 298
BT - RadarCon'11 - In the Eye of the Storm
T2 - 2011 IEEE Radar Conference: In the Eye of the Storm, RadarCon'11
Y2 - 23 May 2011 through 27 May 2011
ER -