三维Gabor滤波器与支持向量机的高光谱遥感图像分类

doi:10.3964/j.issn.1000-0593(2014)08-2218-07

CORC > 北京大学 > 地球与空间科学学院

	三维Gabor滤波器与支持向量机的高光谱遥感图像分类; Hyperspectral Image Classification Based on 3-D Gabor Filter and Support Vector Machines
	冯逍 ; 肖鹏峰 ; 李琦 ; 刘小喜 ; 吴小翠
刊名	光谱学与光谱分析
	2014
关键词	高光谱遥感图像分类三维Gabor滤波器波段选择支持向量机 Hyperspectral remote sensing Image classification Three-dimensional Gabor filter Band selection Support vector machines
DOI	10.3964/j.issn.1000-0593(2014)08-2218-07
英文摘要	根据高光谱遥感图像的特点及二维Gabor滤波器纹理分割的原理,提出了一种基于三维Gabor滤波器的高光谱遥感图像分类方法。三维Gabor滤波器能够对高光谱遥感图像所有波段同时进行滤波,将大量的图像信息抽取为少量的不同尺寸、方向和波谱的响应,极大减少了高光谱遥感图像纹理信息提取的计算量。利用不同方向和尺寸的三维Gabor滤波器对祁连山黑河流域上游地区的Hyperion影像全波段进行滤波处理,获取26个纹理响应特征,并分析不同纹理对不同地物的区分度。利用自动子空间划分的波段指数(BI)进行波段选择,选取不同的波段组合进行试验,寻找最佳降维幅度。按照纹理对不同地物响应的区分度逐一加入三维Gabor纹...; A three-dimensional Gabor filter was developed for classification of hyperspectral remote sensing image. This method is based on the characteristics of hyperspectral image and the principle of texture extraction with 2-D Gabor filters. Three-dimensional Gabor filter is able to filter all the bands of hyperspectral image simultaneously, capturing the specific responses in different scales, orientations, and spectral-dependent properties from enormous image information, which greatly reduces the time consumption in hyperspectral image texture extraction, and solve the overlay difficulties of filtered spectrums. Using the designed three-dimensional Gabor filters in different scales and orientations, Hyperion image which covers the typical area of Qi Lian Mountain was processed with full bands to get 26 Gabor texture features and the spatial differences of Gabor feature textures corresponding to each land types were analyzed. On the basis of automatic subspace separation, the dimensions of the hyperspectral image were reduced by band index (BI) method which provides different band combinations for classification in order to search for the optimal magnitude of dimension reduction. Adding three-dimensional Gabor texture features successively according to its discrimination to the given land types, supervised classification was carried out with the classifier support vector machines (SVM). It is shown that the method using three-dimensional Gabor texture features and BI band selection based on automatic subspace separation for hyperspectral image classification can not only reduce dimensions, but also improve the classification accuracy and efficiency of hyperspectral image.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000339930600040&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; SCI(E); EI; 中文核心期刊要目总览(PKU); 中国科技核心期刊(ISTIC); 中国科学引文数据库(CSCD); 0; fengxiao198995@163.com; xiaopf@gmail.com; 08; 2218-2224; 34
语种	中文
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/48750]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	冯逍,肖鹏峰,李琦,等. 三维Gabor滤波器与支持向量机的高光谱遥感图像分类, Hyperspectral Image Classification Based on 3-D Gabor Filter and Support Vector Machines[J]. 光谱学与光谱分析,2014.
APA	冯逍,肖鹏峰,李琦,刘小喜,&吴小翠.(2014).三维Gabor滤波器与支持向量机的高光谱遥感图像分类.光谱学与光谱分析.
MLA	冯逍,et al."三维Gabor滤波器与支持向量机的高光谱遥感图像分类".光谱学与光谱分析 (2014).