Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images
Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images
复制标题
高光谱图像光谱空间分类的基于超像素的特征特定稀疏表示
DOI：
10.3390/rs11050536
复制
发表时间：
2019-03-01
期刊：
影响因子：
5
通讯作者：
Marshall, Stephen
中科院分区：
文献类型：
--
作者：
Sun, He;Ren, Jinchang;Marshall, Stephen
为了提高稀疏表示分类（SRC）的性能，提出一种基于超像素的特征稀疏表示框架（SPFS-SRC），用于超像素级高光谱图像（HSI）的光谱-空间分类.首先，将HSI划分为不同的空间区域，每个区域都是形状和大小适应的，并被认为是一个超像素。对于每个超像素，它包含具有相似光谱特性的多个像素。由于在HSI分类中利用多个特征已被证明是一种有效的策略，因此我们为每个超像素生成了空间和光谱特征。通过假设超像素中的所有像素都属于某个类，将核SRC引入到HSI的分类中。在SRC框架中，我们采用了度量学习策略来利用不同特征的共性。两个流行的HSI数据集上的实验结果表明，我们提出的方法的有效性。
To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology.