基于自动阈值属性形态剖面光谱空间联合特征编码的高光谱图像分类  

Hyperspectral Image Classification Based on Automatic Threshold Attribute Profiles and Spatial-Spectral Encoding Union Features

作　　者：杨培琦 王明军[1] Yang Peiqi;Wang Mingjun(School of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,Shaanxi,China)

机构地区：[1]西安理工大学自动化与信息工程学院,陕西西安710048

出　　处：《激光与光电子学进展》2023年第12期177-184,共8页Laser & Optoelectronics Progress

基　　金：国家自然科学基金重大研究计划培育项目(92052106);陕西省杰出青年科学基金(2020JC-42)。

摘　　要：高光谱图像分类的有效特征提取仍然是遥感中具有挑战性的研究课题。为了解决这个问题,提出一种基于自动阈值属性形态剖面的光谱空间特征框架用于高光谱图像分类。它包含两个阶段:1)将高光谱图像灰度值转化为树形结构的属性形态剖面,利用所提自动阈值方法进行过滤树操作,以创建最终的扩展多元属性形态剖面,获得空谱特征数据。这种方法不需要自定义任何阈值,仅需要少量的过滤操作就可以获得最大的空间信息。2)将获得的空谱特征数据通过训练的光谱角度映射堆叠式自动编码器网络来创建有效的分类器,得到最终的分类结果。通过在两个真实的高光谱图像数据集上应用这种改进方法并将结果与现有方法进行比较,证明了该方法的有效性。The effective extraction of features for hyperspectral image classification is a challenging research topic in remote sensing.To solve this problem,a spatial-spectral feature framework with an automatic threshold attribute attribute profile is proposed for hyperspectral image classification.The framework includes two stages.The first stage involves conversion of the grayscale value of the hyperspectral image into an attribute morphological profile of the tree structure,filtering the tree using the proposed automatic threshold method to create the final extended multivariate attribute morphological profile,and using the profile to obtain the spatial-spectral feature data.The proposed method does not require the customization of any thresholds and only requires a few filtering operations to obtain the maximum spatial information.Then,in the second stage,the derived spatial-spectral feature data are used to create an effective classifier using a trained spectral angle mapping stackable automatic encoder network to obtain the final classification result.Finally,the effectiveness of the method is verified by applying it to two real hyperspectral image datasets and comparing the results with those of existing methods.

关 键 词：图像处理 高光谱图像 属性形态剖面 自动阈值 深度特征 

分 类 号：O436[机械工程—光学工程]