基于空-谱特征K-means的长波红外高光谱图像分类

高光谱图像(hyper spectral imagery,HSI)分类已成为探测技术的重要研究方向之一,同时也在军事和民用领域得到广泛运用。然而,波段数目巨大、数据冗余、空间特征利用率低等因素已成为高光谱图像分类的挑战,且现有的高光谱分类大多利用可见光或短波红外高光谱数据分类。针对这些问题,本文提出了一种基于光谱和空间特征的K-means分类方法。首先提取空间特征,然后将光谱与空间特征相结合并降维,最后引入K-means算法得到较普通K-means更佳的分类结果。并将此算法运用在长波红外的高光谱图像分类中。

Long-wave Infrared Hyperspectral Image Classification Based on K-means of Spatial-Spectral Features

Hyper spectral image classification has become one of the most important research directions in detection technology;furthermore,it has been widely used in military and civilian fields.However,the significant number of bands,data redundancy,and low utilization of spatial features render the classification of hyper spectral images challenging,and most of existing hyper spectral image classifications use visible light or short-wave infrared data.Hence,a K-means classification method based on spectral and spatial features is proposed in this paper.First,spatial features are extracted;next,the spectral features are combined with the spatial features and the dimensions are reduced.Finally,the K-means algorithm is introduced to obtain classification results that are better than those of normal K-means,and the algorithm is applied to long-wave infrared hyper spectral image classification.