数据采集与处理2018，Vol.33Issue(1)：132-143,12.DOI:10.16337/j.1004-9037.2018.01.015
结合分水岭分割的合成核SVM高光谱分类
Combining Watershed Segmentation with Composite-Kernels for Hyperspectral Image Classification
摘要
Abstract
Hyperspectral images have been widely used in target dectection terrain classification and so on owing to its rich spectral information.Classification,being the fundamental step to further explore the hyperspectral images,attracts wider concern.The spatial information describes the connections between pixels with its spatial neighbors which can help to solve the problems like metameric substance of same spectrum,metameric spectrum of same substance and insufficient labeled samples with a high dimension while the spectral information cannot handle well.The traditional preprocessing uses a structure element to obtain the spatial neighbors and assist the last classification with the extracted spatial features.It is obvious that the structure element matters,however one cannot find a suitable size to meet all demands. For dealing with this,a method combing watershed segmentation with composite-kernels support vector machine(SVM)is prposed.It is the characteristics of over segmentation that we use to get a self-adap-ting spatial neighbors,containing less dissimilar pixels and being more discriminant for every pixel,then we fuse the spatial features and the spectral through the composite-kernels SVM and give a reliable judge-ment.Experiments show that the proposed method can make a better use of the spatial imformation and achieve a high accuracy with limited training samples.关键词
图像分类/高光谱图像/分水岭分割/空间近邻/合成核支持向量机Key words
image classification/hyperspectral image/watershed segmentation/spatial neighbors/com-posite-kernels SVM分类
信息技术与安全科学引用本文复制引用
赵振凯,杨明..结合分水岭分割的合成核SVM高光谱分类[J].数据采集与处理,2018,33(1):132-143,12.基金项目
江苏省自然科学基金重点重大专项(BK2011005)资助项目 （BK2011005）
国家自然科学基金面上(61272222)资助项目 （61272222）
江苏省自然科学基金(BK2011782)资助项目. （BK2011782）
