Hyperspectral Image Classification Using Discrete Space Model and Support Vector Machines

Li Xie; Guangyao Li; Mang Xiao; Lei Peng; Qiaochuan Chen

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Hyperspectral Image Classification Using Discrete Space Model and Support Vector Machines

机译：基于离散空间模型和支持向量机的高光谱图像分类

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摘要

In this letter, a novel method based on discrete space model (DSM) and support vector machines (SVMs) is proposed for hyperspectral image (HSI) classification. The DSM approach transforms continuous spectral signatures into discrete features and constructs a space model with the discrete features. Therefore, the classification capability of SVMs can be improved on account of the discrete feature space. Moreover, a composite kernel model is employed to take advantage of the spectral and spatial features among neighboring pixels. The proposed method is applied to real HSIs for classification. The experimental results confirm that the classification accuracy for the SVMs could be improved using the DSM method prior to classification.

机译：在这封信中，提出了一种基于离散空间模型（DSM）和支持向量机（SVM）的新颖方法，用于高光谱图像（HSI）分类。 DSM方法将连续光谱特征转换为离散特征，并使用离散特征构建空间模型。因此，由于离散特征空间，可以提高SVM的分类能力。此外，采用复合核模型来利用相邻像素之间的光谱和空间特征。所提出的方法被应用于实际的HSI进行分类。实验结果证实，在分类之前，可以使用DSM方法提高SVM的分类精度。

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来源
《IEEE Geoscience and Remote Sensing Letters》 |2017年第3期|374-378|共5页
作者
Li Xie; Guangyao Li; Mang Xiao; Lei Peng; Qiaochuan Chen;
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作者单位

College of Electronics and Information Engineering, Tongji University, Shanghai, China;

College of Electronics and Information Engineering, Tongji University, Shanghai, China;

College of Electronics and Information Engineering, Tongji University, Shanghai, China;

College of Electronics and Information Engineering, Tongji University, Shanghai, China;

College of Electronics and Information Engineering, Tongji University, Shanghai, China;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Support vector machines; Kernel; Training; Information entropy; Hyperspectral imaging; Speckle;

机译：支持向量机;核;训练;信息熵;高光谱成像;散斑;

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3. Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification [J] . Haoyang Yu, Lianru Gao, Wenzhi Liao, IEEE Geoscience and Remote Sensing Letters . 2017,第11期

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4. Support vector machines, import vector machines and relevance vector machines for hyperspectral classification — A comparison [C] . 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing . 2011

机译：支持向量机，导入向量机和相关向量机用于高光谱分类—比较
5. Classification of remote sensing images using support vector machines. [D] . Watanachaturaporn, Pakorn. 2005

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6. Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines [O] . Javier Plaza, Antonio J. Plaza, Cristina Barra 2009

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7. Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields [O] . Benediktsson, Jon Atli, Yu, Haoyang, Gao, Lianru, 2016

机译：基于子空间的支持向量机和自适应马尔可夫随机场的光谱空间高光谱图像分类

Hyperspectral Image Classification Using Discrete Space Model and Support Vector Machines

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