基于自适应核联合稀疏表示的多特征高光谱图像分类

Multifeature hyperspectral image classification based on adaptive kernel joint sparse representation

稀疏表示已被证明是高光谱图像(HSI)分类中的有力工具,同时利用多种特征信息进行联合分类的优点在HSI图像分类领域受到关注,但多特征数据的稀疏策略以及数据的非线性是两个棘手的问题.为此提出了自适应稀疏模式的核联合稀疏模型对高光谱图像进行分类.对于几个互补特征(梯度,文理和形状),该模型同时获取每种特征的表示向量,并且通过施加自适应稀疏策略ladaptive,0来有效利用多特征信息.自适应稀疏策略,不仅限制不同特征空间的像素通过来自特定类的原子表示,而且允许这些像素选定的原子不同,从而提供更好的表示方法.此外,提出的核联合稀疏表示模型用于处理数据的非线性问题.核模型将数据投影到高维空间以提高可分离性,实现比线性模型更好的性能.在数据集Indian Pines和University of Pavia的实验结果表明,所提出的算法表现出更高的分类精度.

Sparse representation has proved to be a powerful tool in hyperspectral image （HSI） classification, and the advantages of joint classification using multifeature information have also attracted in HSI classification field. However, the sparse strategy of multifeature data and the non linearity in data are two difficult problems. A kernel adaptive sparse model is proposed to classify hyperspectral images. For several complementary features （gradient, texture and shape）, the proposed model simultaneously obtains the representation vector for each feature, and utilizes the adaptive sparse strategy ladaptive,0 to effectively use the multifeature information. The adaptive sparse strategy not only limits the representation of pixels in different feature spaces by atoms from a particular class, but also allows the selected atoms of these pixels to be different, thus providing a better representation. In addition, the proposed kernel joint sparse representation model is used to deal with non-linear problems of data. The kernel model projects data into high-dimensional space to improve separability and achieve better performance than linear models. The experimental results of the Indian Pines and University of Pavia show that the proposed algorithm exhibits a higher classification accuracy.