基于MFA与kNNS算法的高光谱遥感影像分类  

Hyperspectral Remote Sensing Image Classification Based on MFA and kNNS

作　　者：王立志[1] 黄鸿[1] 冯海亮[1] 

机构地区：[1]重庆大学光电技术及系统教育部重点实验室,重庆400044

出　　处：《计算机科学》2012年第6期261-265,共5页Computer Science

基　　金：国家自然科学基金(61101168);重庆市科技攻关重点项目(CSTC2009AB2231);重庆市自然科学基金(CSTC2009BB2195)资助

摘　　要：为了研究高光谱影像数据的维数约简和分类问题,提出了一种基于边际费希尔分析(MFA)和kNNS的高光谱遥感影像数据分类算法。该方法利用数据的类别信息,通过MFA将高光谱数据从高维观测空间投影到低维流形空间,然后利用邻域内多个近邻点的信息通过kNNS分类器对低维空间中的数据进行分类。在Urban、Washington和Indian Pine数据集上的分类识别实验表明,该方法能够较为有效地发现高维空间中数据的内蕴结构,在每类随机选取4,6,8个训练样本的情况下,该方法的总体分类精度能够比其他算法提高3.7%～8.5%,分类精度有了明显的提高。In order to explore dimensionality reduction and classification in hyperspectral remote sensing image,an algo- rithm based on marginal Fisher analysis（MFA） and k-nearest-neighbor simplex（kNNS） was proposed in this paper. First, the data were projected from a high-dimensional space onto low-dimensional space by MFA combined with the information of different classes. Then, classification was performed under the kNNS classifier by using a few neighbors from each class. The experimental results on the Urban data set, Washington DC Mall data set and Indian Pine hyper- spectral data set show the effectiveness of the proposed algorithm. When i（i=4,6,8） samples of each class are randomly selected for training and 100 samples of each class for testing, the overall accuracy of our proposed algorithm is improved by 3.7%-8. 5% compared with other methods.

关 键 词：高光谱影像 地物分类 图嵌入框架 最近邻 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置] TP391.4[自动化与计算机技术—控制科学与工程]