CAJ | 학술논문

针对高光谱图像波段多、存在噪声干扰等特点,提出了一种混合Contourlet和主成分分析(principal com-ponent analysis,简称PCA)变换的高光谱图像融合方法.首先将多个波段的高光谱图像进行Contourlet变换,得到系列多尺度、方向各异的子带系数,然后利用PCA变换对各子带系数分别进行自适应融合处理.实验结果表明该算法可以有效地进行高光谱图像融合,消除噪声干扰,获得比直接应用Contourlet变换和PCA变换更好的融合效果.
침대고광보도상파단다、존재조성간우등특점,제출료일충혼합Contourlet화주성분분석(principal com-ponent analysis,간칭PCA)변환적고광보도상융합방법.수선장다개파단적고광보도상진행Contourlet변환,득도계렬다척도、방향각이적자대계수,연후이용PCA변환대각자대계수분별진행자괄응융합처리.실험결과표명해산법가이유효지진행고광보도상융합,소제조성간우,획득비직접응용Contourlet변환화PCA변환경호적융합효과.
According to the characteristics of hyperspectral images such as muhi-band and corrupted by various noises, a nov-el fusion method of hyperspectral images based on hybrid Contottrlet transform and principal component analysis(PCA) transform were proposed. Firstly, Contourlet transform was performed simultaneously on several hyperspectral images to retain the sub-band images at different frequency and directions. Then each sub-band image was adaptively fused by PCA transform. The experiment result shows that the proposed method could well fuse hyperspectral images with noises eliminated and it outperforms the Contourlet and PCA methods.