CAJ | 학술논문

线性判决分析（ LDA）用于图像特征提取时，存在着损失二维空间结构信息、计算复杂度大的缺点。二维线性判决分析（2DLDA）弥补了LDA的缺点，但2DLDA仅消除了图像各列间的相关性，所提取的图像特征维数仍然较大。为解决上述问题，采用双向2DLDA与LDA相结合的特征提取算法对图像的行和列同时进行压缩，减少特征矩阵维数，降低计算量。实验结果表明，所提出的SAR（ Synthetic Aperture Radar）图像目标识别方法有效地降低了图像数据维数，提高了识别率，并克服了方位角变化对识别结果的影响。
선성판결분석（ LDA）용우도상특정제취시，존재착손실이유공간결구신식、계산복잡도대적결점。이유선성판결분석（2DLDA）미보료LDA적결점，단2DLDA부소제료도상각렬간적상관성，소제취적도상특정유수잉연교대。위해결상술문제，채용쌍향2DLDA여LDA상결합적특정제취산법대도상적행화렬동시진행압축，감소특정구진유수，강저계산량。실험결과표명，소제출적SAR（ Synthetic Aperture Radar）도상목표식별방법유효지강저료도상수거유수，제고료식별솔，병극복료방위각변화대식별결과적영향。
Linear discriminant analysis ( LDA) has the disadvantages of information losses in regard to two-dimensional spatial structure and high computational complexity when applying to image feature extraction.Two-dimensional linear discriminant analysis (2DLDA) makes up the flaws of LDA.However, it only eliminates the pertinence between the columns of image, while the number of features extracted is still large.In order to solve the problems, we adopt a new feature extraction algorithm to compress the columns and rows of image simultaneously, which combines the bidirectional 2DLDA with LDA, thus reduces the number of feature matrix dimensions and decreases the calculation amount.Experimental results show that the SAR images recognition method presented in this paper effectively reduces image dimensions numberand raises recognition rate, it also overcomes the impact on recognition result brought by the variation of target azimuth.