15---2015-0192 朱勇.pdf

第18卷 第2 期 Vol. 18, No.2 2016 年2 月 Feb., 2016 引用格式：朱勇,吴波.光谱与空间维双重稀疏表达的高光谱影像分类[J].地球信息科学学报,2016,18(2):263-271. [ Zhu Y, Wu B. Classification of hyperspectral images with spectral-spatial sparse representation. 2016. Journal of Geo-information Science, 18(2): 163-271. ] DOI: 10.3724/SP.J. 1047.2016.00263 光谱与空间维双重稀疏表达的高光谱影像分类 朱 勇，吴 波* 福州大学空间数据挖掘与信息共享教育部重点实验室，福建省空间信息工程研究中心，福州350002 Classification of Hyperspectral Images with Spectral-Spatial Sparse Representation ZHU Yong and WU Bo* Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China Abstract: A novel sparse representation classification model with spectral-spatial sparsity properties is presented to improve the classification accuracy of hyperspectral images. Firstly, this method uses the wavelet dictionary as the core dictionary to extract spectral domain sparse information, and then the spectral dimension sparse representation classification is transformed into the wavelet domain (WSRC) by inverse wavelet transformation. After that, we actually extract the sparse spectral features of the hyper- spectral images and increase the recognition of the original dictionary. Secondly, considering the unity and diversity of the spatial adjacent object, we realize the sparse coding of the neighborhood pixels, and then accumulate the sparse codes. At the same time, we classify the hyperspectral images using a linear classifier that is based on the accumulated sparse codes. This method ensures that we extract the main sparse signal of the neighborhood pixels on the basis of the personality features of sparse encoding, and it perf