Huang Shaoguang, Zhang Hongyan, Pižurica Aleksandra
Department of Telecommunications and Information Processing, Ghent University, Sint Pietersnieuwstraat 41, 9000 Gent, Belgium.
The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan 430079, China.
Sensors (Basel). 2017 Sep 12;17(9):2087. doi: 10.3390/s17092087.
Sparse representation has been extensively investigated for hyperspectral image (HSI) classification and led to substantial improvements in the performance over the traditional methods, such as support vector machine (SVM). However, the existing sparsity-based classification methods typically assume Gaussian noise, neglecting the fact that HSIs are often corrupted by different types of noise in practice. In this paper, we develop a robust classification model that admits realistic mixed noise, which includes Gaussian noise and sparse noise. We combine a model for mixed noise with a prior on the representation coefficients of input data within a unified framework, which produces three kinds of robust classification methods based on sparse representation classification (SRC), joint SRC and joint SRC on a super-pixels level. Experimental results on simulated and real data demonstrate the effectiveness of the proposed method and clear benefits from the introduced mixed-noise model.
稀疏表示已被广泛用于高光谱图像（HSI）分类，并在性能上比传统方法（如支持向量机（SVM））有了显著提升。然而，现有的基于稀疏性的分类方法通常假设为高斯噪声，而忽略了在实际中高光谱图像常常受到不同类型噪声干扰的事实。在本文中，我们开发了一种鲁棒的分类模型，该模型允许存在实际的混合噪声，其中包括高斯噪声和稀疏噪声。我们在一个统一框架内将混合噪声模型与输入数据表示系数的先验相结合，由此产生了基于稀疏表示分类（SRC）、联合SRC以及超像素级联合SRC的三种鲁棒分类方法。在模拟数据和真实数据上的实验结果证明了所提方法的有效性以及引入混合噪声模型所带来的明显优势。
