基于低秩张量分析的高光谱图像降维与分类  

Dimensionality reduction and classification based on lower rank tensor analysis for hyperspectral imagery

作　　者：陈昭[1] 王斌[1,2] 张立明[1] 

机构地区：[1]复旦大学电子工程系,上海200433 [2]复旦大学波散射与遥感信息重点实验室,上海200433

出　　处：《红外与毫米波学报》2013年第6期569-575,共7页Journal of Infrared and Millimeter Waves

基　　金：国家自然科学基金(61071134,41371337);上海市教委科研创新项目(13ZZ005);高等学校博士学科点专项科研基金(20110071110018)~~

摘　　要：提出一种用于高光谱图像降维和分类的分块低秩张量分析方法.该算法以提高分类精度为目标,对图像张量分块进行降维和分类.将高光谱图像分成若干子张量,不仅保存了高光谱图像的三维数据结构,利用了空间与光谱维度的关联性,还充分挖掘了图像局部的空间相关性.与现有的张量分析法相比,这种分块处理方法克服了图像的整体空间相关性较弱以及子空间维度的设定对降维效果的负面影响.只要子空间维度小于子张量维度,所提议的分块算法就能取得较好的降维效果,其分类精度远远高于不分块的算法,从而无需借助原本就不可靠的子空间维度估计法.仿真和真实数据的实验结果表明,所提议分块低秩张量分析算法明显地表现出较好的降维效果,具有较高的分类精度.Sub-tensor based lower rank tensor analysis used for dimenslonallty reduction and classlncatlon m nyperspectral imagery was proposed. The method aims at raising classification accuracy by representing the hyperspectral image as a tensor. The tensor is divided into sub-tensors, wherein, dimensionality reduction and pixel classification were performed. Benefiting from the sub-tensors, the method capitalizes on local spatial correlation, exploits interaction between spatial and spectral dimensions, and maintains hyperspectral data structure with 3D tensor. Compared with existing theo- ries based on tensor analysis, the proposed method eliminates the negative impacts of poor subspace dimension estimation and low global spatial correlation, which might seriously degrade performances of dimensionality reduction. Moreover, as long as subspace dimensions are smaller than sub-tensor dimensions, the method with sub-tensors achieves much higher classification accuracy than the method without sub-tensors. Therefore, for the proposed method, it is not necessary to estimate the subspace dimension. Experimental results of both simulated and real hyperspectral data. demonstrated that sub-tensor based lower rank tensor analysis gives better performance in dimensionality reduction and brings higher classi- fication accuracy than existing methods do.

关 键 词：高光谱图像 低秩张量分析 子张量 空间相关性 子空间维度 降维 分类 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]