基于随机矩阵的高光谱影像非负稀疏表达分类

Random Matrix Based Nonnegative Sparse Representation for Hyperspectral Image Classification

摘要:

考虑到常规的高光谱影像稀疏表达分类模型的不足，本文提出随机矩阵-非负稀疏表达分类模型来提高高光谱影像的分类精度。通过引入随机矩阵来改善传统稀疏表达分类模型中测量矩阵以更好满足限制等距特性条件，同时限定系数向量的非负性以提高重构系数的可解释性。基于两个不同的高光谱数据集，对随机矩阵-非负稀疏表达分类模型采用三种方法进行系数重构，并对比常规稀疏表达分类模型的分类结果。实验证明，文中所提的模型能够明显提高常规稀疏表达分类模型的分类结果。同时，随机矩阵的投影维数对分类精度的影响研究实验表明，较大的投影维数能够保证文中的模型用以提高高光谱影像的分类精度。

Abstract:

Considering the limitations of regular classification model using Sparse Representation (SR), this paper proposes an innovative model named Random Matrix-Nonnegative Sparse Representation (RM-NSR) to improve the classification results of hyperspectral imagery. The RM-NSR model introduces a random matrix inspired by random projection to improve the Restricted Isometry Property (RIP) condition of measurement matrix in the regular SR model. The new model also considers the non-negativity of reconstructed sparse coefficient vectors. Based on Urban and PaviaU hyperspectral datasets, three different schemes in the RM-NSR model are utilized to recovery the sparse efficient and the classification results are compared with that of the regular SR model. Experimental results show that the RM-NSR model obviously outperforms the regular SR model in the average classification accuracies (ACAs). Furthermore, the relationships between projected dimension of random matrix and the ACAs show that a greater projected dimension guarantees the improvement of ACAs by the RM-NSR model.