结合Beltrami流和递归滤波的高光谱图像分类方法  

Hyperspectral Image Classification Method Combined Beltrami Flow and Recursive Filter

作　　者：廖建尚[1] 王立国[2] 郝思媛[3] 

机构地区：[1]广东交通职业技术学院计算机工程学院,广州510650 [2]哈尔滨工程大学信息与通信工程学院,哈尔滨150001 [3]青岛理工大学通信与电子工程学院,青岛266520

出　　处：《农业机械学报》2018年第1期42-50,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(61275010;61675051);国家星火计划项目(2014GA780056);广东省科技计划项目(2017ZC0538);广东交通职业技术学院校级重点科研项目(2017-1-001);广东省高等职业教育品牌专业建设项目(2016gzpp044)

摘　　要：提出一种结合Beltrami流滤波和域转换递归滤波的高光谱图像分类算法(BFRF-SVM)。分别利用Beltrami流对主成分分析(PCA)降维后的高光谱图像滤波方法和域转换递归滤波方法对全光谱波段进行滤波,两种空间信息进行线性融合后交由支持向量机(SVM)完成分类。实验表明,相比使用光谱信息、高光谱降维、空谱结合的SVM分类方法,以及边缘保持滤波和递归滤波以及形态学滤波特征方法,本文提出的BFRF-SVM方法对高光谱图像的分类精度有较大提高,验证了该方法的有效性。In the past,spatial feature extraction of hyperspectral image was usually limited to one feature extraction,and the more comprehensive spatial feature was not obtained. An improved scheme was put forward according to existent methods. An algorithm of classification（BFRF-SVM） was proposed,which was combined with spatial information obtained by Beltrami flow and domain transform recursive filter.Firstly,the spatial feature was extracted by Beltrami flow on hyperspectral data whose dimensions were reduced by principal component analysis（PCA）,and the spatial correlation feature was obtained by domain transform recursive filter for all bands. Secondly,the two kinds of feature were combined,which were classified by SVM. The BFRF-SVM classification method was implemented on the hyperspectral data of Indian Pines and Pavia. The following results were obtained. In the first place,the overall accuracy（OA） of Indian Pines was 96. 01% and that of Pavia was 97. 46%,which were 12 ~15 percentage points higher than that of SVM,12 ~ 16 percentage points higher than that of PCA-SVM,2 ~ 12 percentage points higher than that of SGB-SVM,SBL-SVM and SGD-SVM,4 ~ 5 percentage points higher than that of EPF,1 ~ 3 percentage points higher than that of IFRF,and 2 ~ 6 percentage points higher than that of SMP-SVM,respectively,showing very good performance in hyperspectral classification. In the second place,although the training samples were only 7% of Indian Pines and 3% of Pavia,the OA of both can reach 96. 01% and 97. 46%,respectively,which removed the salt and pepper noise in the classification map obviously. In the last place,although the training samples were reduced to 4% and 0. 5% for Indian Pines and Pavia,the OA can be over 91% and 90%,respectively. When the training samples were increased to 10% and 4. 5%,the OA can exceed 97% and 98%,respectively. The effectiveness of BFRF-SVM was fully verified in the hyperspectral classification with good stability. The experimentsshowed that the BFRF-SVM algorithm was better than

关 键 词：高光谱图像 空间信息 分类 Beltrami流 域转换递归滤波 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]