CAJ | 학술논문

高光谱图像变换域各波段图像噪声强度不同，并具有独特的结构。针对这些特点，该文提出一种基于主成分分析(Principal Component Analysis, PCA)和字典学习的高光谱遥感图像去噪新方法。首先，对高光谱数据进行PCA 变换得到一组主成分图像；然后，对信息量较小的主成分图像分别采用基于自适应字典的稀疏表示方法和对偶树复小波变换方法去除空间维和光谱维的噪声；最后，通过PCA逆变换得出去噪后的数据。结合主成分分析和字典学习的优势，该文方法相对于传统方法对高光谱图像具有更好的自适应性，在细节得到保留的同时有效地抑制了斑块效应。对模拟和实际高光谱遥感图像的实验结果验证了该文方法的有效性。
고광보도상변환역각파단도상조성강도불동，병구유독특적결구。침대저사특점，해문제출일충기우주성분분석(Principal Component Analysis, PCA)화자전학습적고광보요감도상거조신방법。수선，대고광보수거진행PCA 변환득도일조주성분도상；연후，대신식량교소적주성분도상분별채용기우자괄응자전적희소표시방법화대우수복소파변환방법거제공간유화광보유적조성；최후，통과PCA역변환득출거조후적수거。결합주성분분석화자전학습적우세，해문방법상대우전통방법대고광보도상구유경호적자괄응성，재세절득도보류적동시유효지억제료반괴효응。대모의화실제고광보요감도상적실험결과험증료해문방법적유효성。
To reflect different intensities of noises among the different bands in the transform domain and the intrinsic structures of the transformed data, a new approach for denoising the hyperspectral images is proposed based on Principal Component Analysis (PCA) and dictionary learning. At first, a group of the principle component images are achieved by using the PCA transform. Then, these noises which exist in the spatial-and the spectral-domain of the components with low energy are denoised by an adaptively learned dictionary based sparse representation method and the dual-tree complex wavelet transform, respectively. Finally, the denoised data is obtained using the inverse PCA transform. By taking advantages of principal component analysis and dictionary learning, the proposed approach is superior to the traditional ones in preserving the details and alleviating the blocking artifacts. The experiment results on the synthetic and real hyperspectral remote sensing images demonstrate the effectiveness of the proposed approach.