基于特征加权的高光谱图像融合分类

在对高光谱图像监督分类中, 传统的监督学习方法对高光谱数据进行分类时需要获取足够的有标记样本作为训练样本, 这样可以有效的避免Hughes效应. 实际情况下的高光谱数据拥有较多的波段和相对较小的训练样本集给传统的遥感图像分类方法带来了挑战. 因此, 提出了一种基于特征组合以及特征加权的高光谱图像分类算法, 针对纹理特征分析难度较大的现实, 利用一阶直方图的统计特征描述图像纹理特征, 通过类内散度矩阵的逆矩阵作为特征加权矩阵构造组合核函数将高光谱光谱特征和空间特征融合起来, 同时利用特征加权的方法用于提高小训练样本的监督分类精度. 实验结果表明, 本文所提的方法对小样本的高光谱数据分类具有良好的效果.

Fusion Hyperspectral Image Classification Based on Feature Weihting

When supervised classification of hyperspectral images, the traditional supervised learning method for hyperspectral data classification needs to obtain enough samples marked as training samples, which can effectively avoid Hughes effects. Hyperspectral data under actual conditions with more bands and relatively small training set a challenge to the traditional remote sensing image classification. Therefore, this paper presents an approach based on a weighted combination of features and characteristics of hyperspectral image classification algorithm for texture analysis more difficult reality, the use of a first-order statistical characteristics describe the image histogram texture features within class scatter matrix by inverse matrix method as a feature weighting matrix structure combined kernel function hyperspectral spectral characteristics and spatial characteristics integrate, while taking advantage of features to improve the small weighted training samples for supervised classification accuracy. Experimental results show that the method proposed in this paper for a small sample of hyperspectral data classification with good results.