A New Approach for Spectral Feature Extraction and for Unsupervised Clssification of Hyperspectral Data Based on the Gaussian Mixture Model

A. Koltunov; E. Ben-DOR

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A New Approach for Spectral Feature Extraction and for Unsupervised Clssification of Hyperspectral Data Based on the Gaussian Mixture Model

机译：基于高斯混合模型的高光谱数据特征提取与无监督分类新方法

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This paper considers the task of unsupervised classification of hyperspectral data within a Gaussian mixture modeling framework. Different from the traditional clustering techniques That face serious conceptual problems in complex situations, the Gaussian mixture modeling Approach provides a means of solving other simple and complex classification tasks as well as a Way to substantiate results. Despite its theoretical advantages, in practice, the approach is rarely Used for remote sensing because of objective difficulties in its implementation, especially for Hyperspectral data.

机译：本文考虑了在高斯混合建模框架内对高光谱数据进行无监督分类的任务。高斯混合建模方法不同于在复杂情况下面临严重概念问题的传统聚类技术，它提供了解决其他简单和复杂分类任务的方法以及证实结果的方法。尽管具有理论上的优势，但在实践中，由于该方法的实施存在客观困难，特别是对于高光谱数据，这种方法很少用于遥感。

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《Remote Sensing Reviews》 |2001年第2期|p.123-167|共45页
作者
A. Koltunov; E. Ben-DOR;
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正文语种 eng
中图分类遥感技术;
关键词
Hyperspectral remote sensing; Unsupervised classification; Gaussian mixture;

机译：高光谱遥感;无监督分类;高斯混合;

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1. Unsupervised classification of hyperspectral data: an ICA mixture model based approach [J] . CHINTAN A. SHAH, MANOJ K. ARORA, PRAMOD K. VARSHNEY International journal of remote sensing . 2004,第2期

机译：高光谱数据的无监督分类：基于ICA混合模型的方法
2. Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques [J] . Prashanth Reddy Marpu, Mattia Pedergnana, Mauro Dalla Mura, International journal of image and data fusion . 2012,第3期

机译：使用基于有监督和无监督特征提取技术的扩展属性配置文件对高光谱数据进行分类
3. Unsupervised road extraction via a Gaussian mixture model with object-based features [J] . Li Jiayuan, Hu Qingwu, Ai Mingyao International journal of remote sensing . 2018,第8期

机译：通过具有基于对象特征的高斯混合模型进行无监督道路提取
4. Unsupervised Iterative CEM-Clustering Based Multiple Gaussian Feature Extraction for Hyperspectral Image Classification [C] . Bai Xue, Shengwei Zhong, Xiaodi Shang, SPIE Defense + Commercial Sensing Conference . 2019

机译：基于无监督的迭代CEM聚类，用于高光谱图像分类的多个高斯特征提取
5. Extraction, Characterization and Modeling of Network Data Features - A Compressive Sensing and Robust PCA based Approach [D] . Bandara, Vidarshana W. 2015

机译：网络数据特征的提取，表征和建模-基于压缩感知和鲁棒PCA的方法
6. Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging [O] . Qinlin Xiao, Xiulin Bai, Pan Gao, 2020

机译：基于神经网络的应用基于神经网络的特征提取和数据融合来通过可见/短波近红外线和近红外高光谱成像进行地理原产地对地域原产地识别
7. SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery [O] . Junjun Jiang, Jiayi Ma, Chen Chen, 2018

机译：Superpca：超顶篷PCA方法，用于无监督的特征提取高光谱图像
8. Extraction of Chlorophyll-a Concentration Based on Spectral Unmixing Model Using Field Hyperspectral Data in Taihu Lake [R] . Jianguang, W. , Qing, X. , Qinhuo, L. , 2005

机译：基于光谱分离模型的太湖野外高光谱数据提取叶绿素a浓度

A New Approach for Spectral Feature Extraction and for Unsupervised Clssification of Hyperspectral Data Based on the Gaussian Mixture Model

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