ABSTRACT

This paper proposes a novel multi-feature hyperspectral image (HSI) classification framework that utilizes joint sparse representation (JSR) to combine pixel-wise and superpixel-wise features (SMFSR). In this framework, a multi-feature sparse representation algorithm is proposed to exploit different kinds of pixel-wise features. In the meantime, Entropy rate segmentation is utilized to acquire HSI superpixels, which can get harmonious neighbourhood and distinct boundary. SMFSR combines two types of spatial information and is trained for HSI classification. A new solution for SMFSR is proposed which can convert the NP-hard problem of JSR to a convex optimization one. Experimental results on well-known hyperspectral data sets demonstrate that the proposed SMFSR outperforms other commonly used methods.

摘要

本文提出了一种新颖的多特征高光谱图像（HSI）分类框架，该框架利用联合稀疏表示（JSR）组合了像素级和超像素级特征（SMFSR）。在此框架下，提出了一种多特征稀疏表示算法，以利用不同种类的像素特征。同时，利用熵率分割获取HSI超像素，可以得到和谐的邻域和清晰的边界。SMFSR结合了两种类型的空间信息，并经过HSI分类训练。提出了一种新的SMFSR解决方案，可以将JSR的NP难题转化为凸优化问题。在众所周知的高光谱数据集上的实验结果表明，所提出的SMFSR优于其他常用方法。