A Accurate and Robust Automatic Target Recognition Automatic Target Recognition Method for SAR Imagery with Method for SAR Imagery with SOM-based Classification SOM based Classification Shouhei Kidera and Tetsuo Kirimoto University of Electro-Communications, Tokyo, JAPAN International Symposium on Remote Sensing (ISRS) 2012 and International Conference on Space, Aeronautics and Navigation Electronics (ICASANE) 2012, Incheon, Korea, 11 th , Oct., 2012
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AAccurate and Robust Automatic Target RecognitionAutomatic Target Recognition Method for SAR Imagery withMethod for SAR Imagery with
SOM-based ClassificationSOM based ClassificationShouhei Kidera and Tetsuo Kirimoto
University of Electro-Communications, Tokyo, JAPANInternational Symposium on Remote Sensing (ISRS) 2012 and y p g ( )
International Conference on Space, Aeronautics and Navigation Electronics (ICASANE) 2012, Incheon, Korea, 11th, Oct., 2012
IntroductionMicrowave radar : Applicable to adverse weather or darkness
Target recognition with SAR imagery : A great deal of experience is required because SAR image is definitely different from optical image ⇒ ATR(Automatic Target Recognition) method is in demand
Neural Network based approachTraditional ATR methods for SAR imagery
Neural Network based approach ・[1] C. M. Pilcher et al., IEEE Trans. Aerosp. Electron. Syst., 2011⇒ Classification employing range profile dataClassification employing range profile data
・[2] M. Martorella, et al., IEEE Trans. Aerosp. Electron. Syst., 2011⇒ Exploiting ISAR images with full polarimetric datap g g p
Other ATR approach ・[3] Q. Zhao, IEEE Trans. Aerosp. Electron. Syst., 2001⇒ SVM (Support Vector Machine) based classification
P bl i t diti l th dInaccurate classification in the case of
strong noisy situations or observation angle errors
Problem in traditional methods
strong noisy situations or observation angle errors⇒ More robust ATR method is proposed here !
System Model・Mono-static radar system・Targets with arbitrary shapes g y p・Transmitted signal : Frequency sweeping (complex value)・SAR image generation : Back projection algorithm ・Binarization method: Otsu’s discriminant analysis method
SAR image
System Model・Mono-static radar system・Targets with arbitrary shapes g y p・Transmitted signal : Frequency sweeping (complex value)・SAR image generation : Back projection algorithm
Cl ifi i P i i lClassification Principle : Assessing value of integral of U-matrix field from training node
( ) xpyxp −= som;minarg)(ˆ TWinner node for test data x
p Ω∈
Optimal class number is determined :
{ }∫≤≤
=),(),(1
somopt d)(minminarg
trxx
pkCkCNk
sUK
)( pU : U-matrix potential at node p),( xkC : Possible path from k th training node ),( p g
to winner node )(ˆ xp U-matrix field
Procedure of Proposed MethodProcedure of Proposed Method
BLSOM
Experimental Validation Experimental setup :1/200 downscaled model of X-band radar
except for center frequency
・Horn antennas (Beamwidth : 27 deg) ・Frequency range: 24GHz – 40GHz ・Slant range resolution : 9.375 mm ・Aperture Length : 1600 mmSlant range resolution : 9.375 mm Aperture Length : 1600 mm・Off-nadir angle : 54.7 degree ・Tx and Rx Separation : 48 mm
Optical image for 5 civilian airplanes Observation scene in experimentB747 B787
Optical image for 5 civilian airplanes Observation scene in experiment
A320DC-10B777
Evaluation in Noisy Situationva ua o No sy S ua oGaussian noises are numerically
Correct classification probabilityy
added to SAR images for contaminated image generation
U-matrix potential distribution
Robustness to Observation Angle ErrorsRobustness to Observation Angle ErrorsAngle observation error : φ
φφ = 0 deg
Robustness to Observation Angle ErrorsRobustness to Observation Angle ErrorsAngle observation error : φ
φφ = 15 deg
ConclusionConclusion• Accurate ATR method based on Supervised SOM• Accurate ATR method based on Supervised SOM
has been proposed P d th d・Proposed method1. Supervised SOM for ATR classification issue2 New classification metric by using U-matrix metric2. New classification metric by using U-matrix metric
・Experimental validation : ・Correct classification even in under SNR=10 dB・Robust feature for observation angle errors
Future work ・More accurate method exploiting complex value of SAR imageMore accurate method exploiting complex value of SAR image