基于光谱特征和纹理特征协同学习的高光谱图像数据分类

高光谱遥感图像中包含有大量的高维数据,传统的有监督学习算法在对这些数据进行分类时要求获取足够多的有标记样本用于分类器的训练.然而,对高光谱图像中大量的复杂地物像元所属类别进行准确标注通常需要耗费极大的人力.在本文中,我们提出了一种基于半监督学习的光谱和纹理特征协同学习(STF-CT)--法,利用协同学习机制将高光谱图像光谱特征和空间纹理特征这两种不同的特征结合起来,用于小训练样本集下的高光谱图像数据分类问题.STF-CT算法充分利用了高光谱图像的光谱和纹理特征这两个独立视图,构建起一种有效的半监督分类方法,用于提升分类器在小训练样本集情况下的分类精度.实验结果表明该算法在小训练样本集下的高光谱地物分类问题上具有很好的效果.

Hyperspectral Data Classification with Spectral and Texture Features by Co-training Algorithm

Obtaining labeled training sets for hyperspectral image data classification is often time consuming and expensive. Therefore, classification of hyperspectral data with insufficient training samples catches the attention of researchers lately. A spectral and texture feature co-training algorithm is proposed based on a semi-supervised classification scheme. The two views of spectral and spatial information of hyperspectral imagery are combined by using Co-training mechanism. Our algorithm is well suited for the hyperspectral image data classification problem, especially when the size of training samples is small. Experimental results on real data demonstrate that the algorithm can yield good results in land-cover classification with hyperspectral image data.