Research on Semisupervised Classification for Hyperspectral Image Based on Spectral-Spatial Feature

高光谱遥感图像分类是图像研究领域的新热点问题，具有广泛的应用前景。随着近年来关注度的持续上升和研究投入的不断增加，已经取得一定的技术成果。然而，由于高光谱遥感图像本身的特殊性，使得其分类技术存在着维度高而训练集小、信息冗余、混合像素多等问题。针对这些问题，本文开展了基于谱域-空间特征的半监督高光谱图像分类方法研究，主要研究工作及创新点如下： 1.为了充分利用目标像素点的空间信息，提出将谱域和空间信息相结合的特征。目标像素点的空间信息包括：空间邻域像素点光谱信息和目标像素点自身空间位置信息。针对空间邻域像素点光谱信息对目标像素点的分类起积极辅助作用这一发现，提出了基于谱域-空域相结合的新特征，...Hyperspectral remote sensing image classification is a new hot topic in the field of image research and has broad application prospects. Recently, hyperspectral image classification technology has made a certain number of technological achievements with the increasing attention and investment in this research. Nevertheless, there are still some challenges in this classification technology due to t...学位：工学硕士院系专业：信息科学与技术学院_智能科学与技术学号：3152012115301

Hyperspectral image classification based on spectral-spatial combination features and graph cut

高光谱图像中存在着特征维度高而训练集小的问题。为解决该问题,提出了一种2步走的分类方法:1)通过支持向量机对图像进行初步分类,根据分类结果计算出每个类别的均值特征;2)使用1)计算出来的均值特征作为能量函数的数据项,然后利用图割原理对图像做二次分类。实验中发现:空间上相近的像素点往往具有相似的特征,且属于同一个类别。针对这种现象,提取一个将谱域特征和空域特征相结合的新特征。该特征既包含了光谱信息也包含了空间信息,具有较好的分类性能和鲁棒性。在IndIAn PInE数据集和PAVIA unIVErSITy数据集进行实验,实验结果表明了本文提出方法的有效性。The high-dimension of the feature vs.small-size of training set is an unsolved problem in the hyperspectral image classification task.To solve this problem a two-step classification method is proposed.Firstly,a preliminary classification is performed by the support vector machine( SVM) and the classification results are used to calculate the mean feature( MF) of each class.Secondly,a classification based on the graph cut theory is applied with the MFs as an input of the energy function.The experimental results showed that spatially nearby pixels have large possibilities of having the same label and similar features.Therefore,a new feature called spectral-spatial combination( SSC) is extracted that combines the spectral-based feature and spatial-based feature.The SSC feature contains the related spectral and spatial information of each pixel and provides better classification performance and robustness.Experiment results on the Indian Pine dataset and the Pavia University dataset demonstrated the effectiveness of the proposed method.国家自然科学基金资助项目(61202143); 福建省自然科学基金资助项目(2013J05100;2010J01345;2011J01367); 湖南省自然科学基金资助项目(12JJ2040