基于图像重构特征融合的高光谱图像分类方法  

Hyperspectral Image Classification Method Based on Image Reconstruction Feature Fusion

作　　者：刘嘉敏[1] 郑超 张丽梅 邹泽华 Liu Jiamin;Zheng Chao;Zhang Limei;Zou Zehua(Key Laboratory of Optoelectronic Technique System of the Ministry of Education,Chongqing University,Chongqing 400044,China)

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：《中国激光》2021年第9期197-206,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金(41371338);重庆市基础研究与前沿探索项目(cstc2018jcyjAX0093);重庆市研究生科研创新项目(CYB19039)。

摘　　要：针对传统的高光谱遥感图像分类方法未能充分利用空间信息,提出一种基于高光谱图像重构特征融合的分类方法。该方法首先将图像的每个像素点进行LBP(Local Binary Patterns)特征提取,得到每个像素点的LBP特征值;其次提取出每个像素点的空间邻域块,按照图像已知的标签信息去除每个空间邻域块中冗余的背景像素点,得到新的空间邻域块,利用光谱距离得到每个像素点的权重值并计算重构特征值;然后,将像素点的LBP特征值和重构特征值进行叠加融合,获得重构特征融合值;最后,采用K最近邻分类器将像素点进行分类,根据测试样本点和训练样本点的欧氏距离判断测试样本点的类别。在Indian Pines和Pavia University数据集上进行实验。实验结果表明,所提方法的分类精度分别达到99.06%和99.73%。Objective Hyperspectral remote-sensing images contain abundant information and provide a large amount of data.For this reason,hyperspectral remote-sensing imaging is widely used in environmental detection,target recognition,and other fields.This paper focuses on feature extraction and classification methods for hyperspectral images.The traditional classification method does not fully utilize the spatial information in hyperspectral datasets and tends to ignore the effect of background points on the classification.The present paper proposes a classification based on feature fusion using a hyperspectral image reconstruction method.The fused features fully include the spatial information of the data image.The method accurately classifies the images in the Indian Pines and Pavia University datasets.Our basic strategy and findings are anticipated to assist the design of new classification methods of hyperspectral images.Methods The proposed method fuses the features extracted by image reconstruction.The method first extracts the local binary patterns(LBPs)of each pixel to obtain the LBP feature value.Second,it extracts the spatial neighborhood block of each pixel and removes the redundant background pixels in each block based on the known label information of the image.The result is a new spatial neighborhood block.Each pixel is weighted by the spectral distance,and its characteristic value is calculated and reconstructed.The LBP eigenvalue of each pixel and its reconstructed eigenvalue are superimposed into a reconstructed fused eigenvalue.Finally,the pixels are classified by a K nearest neighbor(KNN)classifier,and the type of each test sample point is determined by the Euclidean distance between the test sample and the training samples.The classification performance of the method is experimentally evaluated on the two hyperspectral datasets from the Indian Pines and Pavia University.Results and Discussions The classification performances of our method and several existing methods are evaluated by the Kappa coefficie

关 键 词：遥感 高光谱遥感 LBP特征 空间邻域块 特征融合 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置] P407.8[自动化与计算机技术—控制科学与工程]