Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

Chi M.; Kun Q.; Benediktsson J. A.; Feng R.

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Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

机译：高光谱遥感数据集成分类算法

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摘要

In real applications, it is difficult to obtain a sufficient number of training samples in supervised classification of hyperspectral remote sensing images. Furthermore, the training samples may not represent the real distribution of the whole space. To attack these problems, an ensemble algorithm which combines generative (mixture of Gaussians) and discriminative (support cluster machine) models for classification is proposed. Experimental results carried out on hyperspectral data set collected by the reflective optics system imaging spectrometer sensor, validates the effectiveness of the proposed approach.

机译：在实际应用中，在高光谱遥感图像的监督分类中很难获得足够数量的训练样本。此外，训练样本可能无法代表整个空间的真实分布。为了解决这些问题，提出了一种将生成模型（高斯混合）和判别模型（支持集群机器）相结合的集成算法进行分类。对反射光学系统成像光谱仪传感器收集的高光谱数据集进行的实验结果验证了该方法的有效性。

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《Geoscience and Remote Sensing Letters, IEEE》 |2009年第4期|p.762-766|共5页
作者
Chi M.; Kun Q.; Benediktsson J. A.; Feng R.;
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正文语种 eng
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关键词
Ensemble classification; hyperspectral remote sensing images; mixture of Gaussians (MoGs); support cluster machine (SCM);

机译：集合分类;高光谱遥感影像;高斯混合（MoGs）;支持集群机（SCM）;

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1. Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data [J] . XIN MIAO, JILL S. HEATON, SONGFENG ZHENG, International journal of remote sensing . 2012,第5a6期

机译：基于树的集成算法在多时相多源遥感数据分类中的应用
2. Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data [J] . Xin Miaoa* Jill S. Heatonb Songfeng Zhengc David A. Charletd, Hui Liue International Journal of Remote Sensing . 2012,第6期

机译：基于树的集成算法在多时相多源遥感数据分类中的应用
3. Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms [J] . Shang X., Chisholm L.A. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of . 2014,第6期

机译：利用高光谱遥感和机器学习分类算法对澳大利亚本土森林物种进行分类
4. Classification of Hyperspectral Remote Sensing Images by an Ensemble of Support Vector Machines Under Imbalanced Data [C] . Laxmi Narayana Eeti, Krishna Mohan Buddhiraju IEEE International Geoscience and Remote Sensing Symposium . 2018

机译：不平衡数据下支持向量机集合对高光谱遥感影像的分类
5. Hyperclass: an unsupervised classification method for hyperspectral remotely sensed data. [D] . Lewis, Mark David. 2000

机译：超类：一种用于高光谱遥感数据的无监督分类方法。
6. Classification of Rotylenchulus reniformis Numbers in Cotton Using Remotely Sensed Hyperspectral Data on Self-Organizing Maps [O] . Rushabh A. Doshi, Roger L. King, Gary W. Lawrence 2010

机译：利用自组织图上的遥感高光谱数据对棉花轮虫的数量进行分类
7. Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment [O] . Warren MA, Taylor BH, Grant MG, 2014

机译：使用机载处理库（APL）的遥感机载高光谱数据的数据处理：地球校正算法描述和空间准确性评估

Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

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