雷达科学与技术
雷達科學與技術
뢰체과학여기술
RADAR SCIENCE AND TECHNOLOGY
2014年
1期
44-50,57
,共8页
目标识别%SAR图像%稀疏表示%主元分析%小波分解
目標識彆%SAR圖像%稀疏錶示%主元分析%小波分解
목표식별%SAR도상%희소표시%주원분석%소파분해
target recognition%SAR images%sparse representation%principal component analysis%wavelet decomposition
针对稀疏表示识别算法在图像域构造冗余字典时过分依赖预处理及原子维数较大的问题，提出基于小波字典的 SAR图像稀疏表示识别算法。首先采用二维离散小波变换将原始图像变换到小波域，建立小波域 SAR图像特征模型，得出小波域低频成分可充分表征目标类别信息的结论。然后取小波域低频成分进行2DPCA特征抽取构造小波字典，最后由改进 OMP 算法稀疏分解系数得到识别结果。SAR MSTAR数据的实验结果表明，在无预处理的情况下识别率高达99％，并且在含噪比10％的情况下识别率仍达96％。
針對稀疏錶示識彆算法在圖像域構造冗餘字典時過分依賴預處理及原子維數較大的問題，提齣基于小波字典的 SAR圖像稀疏錶示識彆算法。首先採用二維離散小波變換將原始圖像變換到小波域，建立小波域 SAR圖像特徵模型，得齣小波域低頻成分可充分錶徵目標類彆信息的結論。然後取小波域低頻成分進行2DPCA特徵抽取構造小波字典，最後由改進 OMP 算法稀疏分解繫數得到識彆結果。SAR MSTAR數據的實驗結果錶明，在無預處理的情況下識彆率高達99％，併且在含譟比10％的情況下識彆率仍達96％。
침대희소표시식별산법재도상역구조용여자전시과분의뢰예처리급원자유수교대적문제，제출기우소파자전적 SAR도상희소표시식별산법。수선채용이유리산소파변환장원시도상변환도소파역，건립소파역 SAR도상특정모형，득출소파역저빈성분가충분표정목표유별신식적결론。연후취소파역저빈성분진행2DPCA특정추취구조소파자전，최후유개진 OMP 산법희소분해계수득도식별결과。SAR MSTAR수거적실험결과표명，재무예처리적정황하식별솔고체99％，병차재함조비10％적정황하식별솔잉체96％。
This paper proposes a sparse representation classification(SRC)algorithm of SRA images based on wavelet dictionary.The proposed algorithm can solve the problems of high atom dimensionality and over-dependence on preprocessing when a redundant dictionary is being created by SRC algorithm in image domain.First,original image is transformed into wavelet domain by two-dimensional discrete wavelet trans-formation.And the SAR image feature model of wavelet domain is constructed and a conclusion is drawn that only low frequency component fully represents the target information.Then a wavelet dictionary is construc-ted through 2DPCA feature extraction by using the low-frequency component.Finally,the recognition result is obtained by sparse decomposition coefficients with the modified OMP algorithm.Experiment result based on MSTAR SAR image data shows that the recognition rate is as high as 9 9% without image preprocessing, and even in case of noise ratio of 10% the recognition rate is still 96%.
