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Published： 07 February 2024 ，
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黄媛，贺新光，万义良.2024.联合超像素降维和后处理优化的高光谱图像分类方法.遥感学报，28（2）： 494-510
Huang Y，He X G and Wan Y L. 2024. Hyperspectral-image classification method combining superpixel dimension reduction with post-processing optimization. National Remote Sensing Bulletin， 28（2）：494-510
针对高光谱图像样本标签量少且空间—光谱信息利用不充分而导致图像分类精度较低的问题，本文提出一种联合超像素降维和后验概率优化的高光谱图像分类方法。该方法首先基于高光谱图像的空间—光谱信息为每个样本构建局部邻域集合，并从局部邻域集合中提取超像素稀疏混合特征来充分表征图像的空谱信息和相关变化信息，然后将全局稀疏混合特征输入支持向量机分类器中生成像素的类别概率向量，最后采用后验概率模型优化类别概率向量，并依据概率最大值得到分类标签图。在3组常用的小规模数据集Indian Pines、Pavia University和Salinas以及一组大规模数据集HoustonU上的实验结果表明：本研究所提出的分类方法能够自适应地充分提取高光谱图像的高判别性特征信息，且在少量样本标签情形下，该方法在这4组实验数据集上分别获得了98.58%、96.88%、98.54%和91.01%的总体分类精度，优于文中对比的7种先进分类方法。
Hyperspectral image (HSI) classification is one of the fundamental tasks in the field of applied remote sensing. As technological advances have increased the spatial and spectral resolutions available for data acquisition
the problem of achieving accurate HSI classification is becoming more challenging. This problem is especially true for the HSI data with small labeled training samples and insufficient utilization of spatial-spectral information in HSI classification models. Aiming at these problems
this paper proposes a new HSI classification method (expressed as SKERW_SVM) by combining the Superpixel Dimension Reduction (SDR) with post-processing optimization.
First
we develop a Superpixel Sparse Linear Discriminant Analysis (SSLDA) method by combining Regional Clustering (RC) with SLDA. In the SSLDA method
the RC is applied to construct a homogeneous local neighborhood set with high spatial correlation and spectral similarity for each pixel of the HSI. The SLDA is used to extract superpixel sparse mixture features that can fully characterize spatial-spectral information and related change information of the HSI based on the constructed homogeneous regions. Then
the extracted sparse mixture features are inputted into the support vector machine to generate the class probabilities of all pixels. Finally
the original class probabilities are optimized in the post-processing step by the extended random walker that can express the spatial relationship among adjacent pixels quantitatively. The classification map is obtained according to the maximum probability.
To assess the performance of the proposed method
a series of experiments is conducted on three small-scale HSI datasets
including Indian Pines
University of Pavia
and Salinas
as well as a large-scale HSI dataset HoustonU. The proposed SKERW_SVM obtains overall accuracies of 98.58%
96.88%
98.54%
and 91.01% on Indian Pines
University of Pavia
Salinas
and HoustonU
respectively. Experimental results demonstrate that our SKERW_SVM can fully mine the joint spatial-spectral features of HSI and achieve higher classification accuracy under the case of small labeled training samples compared with several related advanced methods. Moreover
the operation time consumed by SKERW_SVM is more appropriate than that by other methods.
Under the lack of the labeled HSI pixel condition
the proposed HSI classification method by combining the SDR with post-processing optimization can efficiently extract the high-discrimination mixture feature information of HSI and significantly enhance classification performance. The SDR based on the homogeneous local regions
one of the components of the SKERW_SVM classification model
can greatly reduce the data redundancy and fully extract the information of spatial and spectral signatures compared with pixel-wise dimension-reduction methods. Meanwhile
the extended random walker in the post-processing step can fully use the spatial information of HSI by constructing a relationship graph to optimize the original class probabilities
thereby further improving the classification performance.
遥感高光谱图像分类超像素降维混合特征提取后处理优化支持向量机
remote sensinghyperspectral image classificationsuperpixel dimension reductionmixture feature extractionpost-processing optimizationsupport vector machine
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