不变矩在高光谱图像空谱分类中的应用研究  

Research of Hyperspectral Image Spectral-spatial Classification Based on Moment Invariants

作　　者：李铁[1] 张新君[1,2] 

机构地区：[1]辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛125105 [2]大连理工大学计算机科学与技术学院,辽宁大连116024

出　　处：《小型微型计算机系统》2017年第7期1608-1613,共6页Journal of Chinese Computer Systems

摘　　要：提出一种新的有效的空谱分类方法用于高光谱图像分类.结合光谱和纹理特征来提高分类精度.在以像素为中心的小窗口内计算不变矩得到像素的纹理特征.把纹理和光谱特征串联在一起构成一个联合特征向量,用该向量与支持向量机(SVM)一起做分类.在三个高光谱数据集上做实验,并与一些其他的空谱分类方法做比较.结果表明所提出的方法与传统的光谱方法相比,分类精度有明显的提高.在分类精度和计算复杂度上该方法也优于其他的空谱分类方法.结果还表明在小的训练集上所提出的方法能得到很好的分类精度.This paper presents a novel and efficient spectral-spatial classification method for hyperspectral images. It combines the spectral and texture features to improve the classification accuracy. The moment invariants are computed within a small window centered at the pixel to determine pixel-wise texture features. The texture and spectral features are concatenated to form a joint feature vector that is used for classification with support vector machine （ SVM ）. The experiments are carried out on three hyperspectral datasets and results are compared with some other spectral-spatial techniques. The results indicate that the proposed method statistically significantly improved the classification accuracies over the conventional spectral method. The new method has also outperformed the other recently used spectral-spatial methods in terms of both classification accuracies and computational cost. The results also showed that the proposed method can produce good classification accuracy with smaller training sets.

关 键 词：高光谱图像分类 不变矩 空谱特征 支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]