Superpixel and subspace projection-based support vector machines for hyperspectral image classification

Qiong Ran; Haoyang Yu; Lianru Gao; Wei Li; Bing Zhang

doi:10.11834/jig.170201

Remote Sensing Image Processing | Views : 0 下载量: 4 CSCD: 12

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Superpixel and subspace projection-based support vector machines for hyperspectral image classification
Vol. 23, Issue 1, Pages: 95-105(2018)
Received：05 July 2017，

Revised：2017-9-19，

Published：16 January 2018
DOI： 10.11834/jig.170201
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DOI：

Qiong Ran, Haoyang Yu, Lianru Gao, Wei Li, Bing Zhang. Superpixel and subspace projection-based support vector machines for hyperspectral image classification[J]. Journal of Image and Graphics, 2018, 23(1): 95-105. DOI： 10.11834/jig.170201.

摘要

目的

高光谱图像包含了丰富的空间、光谱和辐射信息，能够用于精细的地物分类，但是要达到较高的分类精度，需要解决高维数据与有限样本之间存在矛盾的问题，并且降低因噪声和混合像元引起的同物异谱的影响。为有效解决上述问题，提出结合超像元和子空间投影支持向量机的高光谱图像分类方法。

方法

首先采用简单线性迭代聚类算法将高光谱图像分割成许多无重叠的同质性区域，将每一个区域作为一个超像元，以超像元作为图像分类的最小单元，利用子空间投影算法对超像元构成的图像进行降维处理，在低维特征空间中执行支持向量机分类。本文高光谱图像空谱综合分类模型，对几何特征空间下的超像元分割与光谱特征空间下的子空间投影支持向量机（SVMsub），采用分割后进行特征融合的处理方式，将像元级别转换为面向对象的超像元级别，实现高光谱图像空谱综合分类。

结果

在AVIRIS（airbone visible/infrared imaging spectrometer）获取的Indian Pines数据和Reflective ROSIS（optics system spectrographic imaging system）传感器获取的University of Pavia数据实验中，子空间投影算法比对应的非子空间投影算法的分类精度高，特别是在样本数较少的情况下，分类效果提升明显；利用马尔可夫随机场或超像元融合空间信息的算法比对应的没有融合空间信息的算法的分类精度高；在两组数据均使用少于1%的训练样本情况下，同时融合了超像元和子空间投影的支持向量机算法在两组实验中分类精度均为最高，整体分类精度高出其他相关算法4%左右。

结论

利用超像元处理可以有效融合空间信息，降低同物异谱对分类结果的不利影响；采用子空间投影能够将高光谱数据变换到低维空间中，实现有限训练样本条件下的高精度分类；结合超像元和子空间投影支持向量机的算法能够得到较高的高光谱图像分类精度。

Abstract

Objective

Hyperspectral image contains abundant spatial

spectral

and radiant information and can be used for precise earth object classification. The imbalance between high-dimensional data and limited samples should be solved to obtain accurate classification results of ground objects. The influence of "same object with different spectra" caused by noise and mixing pixels should also be reduced. To solve the aforementioned problems effectively

this study proposes a superpixel and subspace projection-based support vector machine (SVM) method (SP-SVMsub) for hyperspectral image classification.

Method

The framework foundation is the object-based image classification (OBIC)

which is a widely used classification method that includes spatial information. OBIC performs classification after segmentation

and each segment can be regarded as the smallest element in the classification process. The result of over-segmentation can be referred to as a superpixel

which represents the local neighborhood information in an adaptive domain. This study proposes to integrate superpixel segmentation with subspace-based SVM (SVMsub) for hyperspectral image classification. The proposed method can be implemented in three steps. First

simple linear iterative clustering is used to segment a hyperspectral image into several nonoverlapping homogeneous regions

and each region can be considered a superpixel. Second

subspace projection is adopted as a dimensionality reduction method for the image composed of superpixels and the original image. Third

SVM is implemented for classification with the obtained low-dimensional feature space. Innovation:A new spectral-spatial hyperspectral image classification approach is presented in this study. In spatial domain

the original hyperspectral image can be integrated with a segmentation map by applying a feature fusion process such that a pixel-level image is represented by superpixel-level data sets. In spectral domain

SVMsub is adopted to obtain final classification maps. v

Result

In the experiments with data sets collected by using an Airborne Visible/Infrared Imaging Spectrometer over the Indian Pines region in America and a Reflective Optics Spectrographic Imaging System over the University of Pavia in Italy

the accuracies of algorithms with subspace projection are higher than those without subjection projection

and remarkable improvements are shown in cases with few samples. Algorithms that integrate spatial information

either by using Markov random field or superpixel

can acquire higher classification accuracy than those without spatial information. In the case in which less than 1% training samples of two data sets are used

SP-SVMsub obtains the highest classification accuracy. The overall accuracy of SP-SVMsub is approximately 4% higher than that of other related methods.

Conclusion

Superpixel can be used to integrate spatial information and effectively reduce the influence of "same object with different spectra" on classification results. Subspace projection can transform hyperspectral data to a low-dimensional space and can achieve high classification accuracy with limited samples. SP-SVMsub can achieve high classification accuracy for hyperspectral images.

关键词

Keywords

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