摘要
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。
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.
出处
《电子与信息学报》
EI
CSCD
北大核心
2014年第11期2723-2729,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61271294
60872138)资助课题
关键词
图像处理
高光谱图像
去噪
主成分分析
稀疏表示
字典学习
Image processing
Hyperspectral image
Denoising
Principal Component Analysis （PCA）
Sparse representation
Dictionary learning
