结合分水岭分割的合成核SVM高光谱分类

摘要

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.%高光谱图像丰富的光谱信息使其在目标检测、地物分类等领域都具有重要应用,分类作为高光谱应用的重要中间步骤引起了广泛关注.高光谱图像空间信息刻画了光谱像素点与近邻关系,可以较好地弥补单纯使用光谱信息难以解决的同物异谱、同谱异物以及高维小样本等问题.传统预处理方式空间信息的使用是基于固定结构(如方窗)选择空间近邻以计算空间特征辅助分类,但会因窗口大小而影响空间特征质量.为此本文提出了结合分水岭分割的合成核支持向量机(Support vector machine,SVM)高光谱分类,根据分水岭分割图自适应选择优质的空间近邻,然后通过合成核SVM有效地把空间信息融入到原光谱信息分类中.实验表明,本文方法更好地利用了空间信息,实现在少量样本下高光谱图像的快速高精度分类.