联合超像素降维和后处理优化的高光谱图像分类方法
Hyperspectral-image classification method combining superpixel dimension reduction with post-processing optimization
- 2024年28卷第2期 页码：494-510
纸质出版日期： 2024-02-07
DOI： 10.11834/jrs.20221497
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摘要
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。
Abstract
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.
关键词
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
Keywords
remote sensinghyperspectral image classificationsuperpixel dimension reductionmixture feature extractionpost-processing optimizationsupport vector machine
references
Achanta R, Shaji A, Smith K, Lucchi A, Fua P and Süsstrunk S. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11): 2274-2282 [DOI: 10.1109/TPAMI.2012.120http://dx.doi.org/10.1109/TPAMI.2012.120]
Blanzieri E and Melgani F. 2008. Nearest neighbor classification of remote sensing images with the maximal margin principle. IEEE Transactions on Geoscience and Remote Sensing, 46(6): 1804-1811 [DOI: 10.1109/TGRS.2008.916090http://dx.doi.org/10.1109/TGRS.2008.916090]
Camps-Valls G and Bruzzone L. 2005. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1351-1362 [DOI: 10.1109/TGRS.2005.846154http://dx.doi.org/10.1109/TGRS.2005.846154]
Carrino T A, Crósta A P, Toledo C L B and Silva A M. 2018. Hyperspectral remote sensing applied to mineral exploration in southern Peru: a multiple data integration approach in the Chapi Chiara gold prospect. International Journal of Applied Earth Observation and Geoinformation, 64: 287-300 [DOI: 10.1016/j.jag.2017.05.004http://dx.doi.org/10.1016/j.jag.2017.05.004]
Cheng W J and Chen W Q. 2020. Hyperspectral image classification based on MCFFN-Attention. Computer Engineering and Applications, 56(24): 201-206
程文娟, 陈文强. 2020. 基于MCFFN-Attention的高光谱图像分类. 计算机工程与应用, 56(24): 201-206 [DOI: 10.3778/j.issn.1002-8331.1911-0102http://dx.doi.org/10.3778/j.issn.1002-8331.1911-0102]
Dong A G, Zhang Q, Liu H C and Liang M M. 2019. Hyperspectral image classification based on TSNE and multiscale sparse auto-encoder. Computer Engineering and Applications, 55(21): 177-182, 219
董安国, 张倩, 刘洪超, 梁苗苗. 2019. 基于TSNE和多尺度稀疏自编码的高光谱图像分类. 计算机工程与应用, 55(21): 177-182, 219 [DOI: 10.3778/j.issn.1002-8331.1903-0155http://dx.doi.org/10.3778/j.issn.1002-8331.1903-0155]
Dong X F, Gan F P, Li N, Yan B K, Zhang L, Zhao J Q, Yu J C, Liu R Y and Ma Y N. 2020. Fine mineral identification of GF-5 hyperspectral image. Journal of Remote Sensing (Chinese), 24(4): 454-464
董新丰, 甘甫平, 李娜, 闫柏琨, 张磊, 赵佳琪, 于峻川, 刘镕源, 马燕妮. 2020. 高分五号高光谱影像矿物精细识别. 遥感学报, 24(4): 454-464 [DOI: 10.11834/jrs.20209194http://dx.doi.org/10.11834/jrs.20209194]
Duan P H, Ghamisi P, Kang X D, Rasti B, Li S T and Gloaguen R. 2021. Fusion of dual spatial information for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 59(9): 7726-7738 [DOI: 10.1109/TGRS.2020.3031928http://dx.doi.org/10.1109/TGRS.2020.3031928]
Felzenszwalb P F and Huttenlocher D P. 2004. Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2): 167-181 [DOI: 10.1023/B:VISI.0000022288.19776.77http://dx.doi.org/10.1023/B:VISI.0000022288.19776.77]
Fu Q, Guo C and Luo W L. 2020. A hyperspectral image classification method based on spectral-spatial features. Laser and Optoelectronics Progress, 57(20): 202803
付青, 郭晨, 罗文浪. 2020. 一种利用空谱联合特征的高光谱图像分类方法. 激光与光电子学进展, 57(20): 202803 [DOI: 10.3788/LOP57.202803http://dx.doi.org/10.3788/LOP57.202803]
Gao K L, Yu X C, Song Z H, Zhang J, Liu B and Sun Y F. 2021. Deep capsule network combined with spatial-spectral information for hyperspectral image classification. National Remote Sensing Bulletin, 25(6): 1257-1269
高奎亮, 余旭初, 宋治杭, 张晋, 刘冰, 孙一帆. 2021. 联合空谱信息的高光谱影像深度胶囊网络分类. 遥感学报, 25(6): 1257-1269 [DOI: 10.11834/jrs.20210309http://dx.doi.org/10.11834/jrs.20210309]
Grady L. 2006. Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(11): 1768-1783 [DOI: 10.1109/TPAMI.2006.233http://dx.doi.org/10.1109/TPAMI.2006.233]
Hua Y, Zhang T, Xi H W, Wang Y F and Huang X L. 2012. Research on method of hyperspectral remote sensing image classification based on decision tree. Computer Technology and Development, 22(6): 198-202
华晔, 张涛, 奚后玮, 王玉斐, 黄秀丽. 2012. 基于决策树的高光谱遥感影像分类方法研究. 计算机技术与发展, 22(6): 198-202
Jiang J J, Ma J Y, Chen C, Wang Z Y, Cai Z H and Wang L Z. 2018. SuperPCA: a superpixelwise PCA approach for unsupervised feature extraction of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4581-4593 [DOI: 10.1109/TGRS.2018.2828029http://dx.doi.org/10.1109/TGRS.2018.2828029]
Kang X D, Li S T, Fang L Y, Li M X and Benediktsson J A. 2015. Extended random walker-based classification of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(1): 144-153 [DOI: 10.1109/TGRS.2014.2319373http://dx.doi.org/10.1109/TGRS.2014.2319373]
Li C N, Shao Y H, Yin W T and Liu M Z. 2020. Robust and sparse linear discriminant analysis via an alternating direction method of multipliers. IEEE Transactions on Neural Networks and Learning Systems, 31(3): 915-926 [DOI: 10.1109/TNNLS.2019.2910991http://dx.doi.org/10.1109/TNNLS.2019.2910991]
Li D, Kong F Q and Zhu D Y. 2021. Hyperspectral image classification based on local Gaussian mixture feature extraction. Acta Optica Sinica, 41(6): 0610001
李丹, 孔繁锵, 朱德燕. 2021. 基于局部高斯混合特征提取的高光谱图像分类. 光学学报, 41(6): 0610001 [DOI: 10.3788/AOS202141.0610001http://dx.doi.org/10.3788/AOS202141.0610001]
Li F Y, Huo H T, Li J and Bai J. 2019. Hyperspectral image classification via multiple-feature-based improved sparse representation. Acta Optica Sinica, 39(5): 0528004
李非燕, 霍宏涛, 李静, 白杰. 2019. 基于多特征和改进稀疏表示的高光谱图像分类. 光学学报, 39(5): 0528004 [DOI: 10.3788/AOS201939.0528004http://dx.doi.org/10.3788/AOS201939.0528004]
Li W, Chen C, Su H J and Du Q. 2015. Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 53(7): 3681-3693 [DOI: 10.1109/TGRS.2014.2381602http://dx.doi.org/10.1109/TGRS.2014.2381602]
Liang L, Di L P, Zhang L P, Deng M X, Qin Z H, Zhao S H and Lin H. 2015. Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sensing of Environment, 165: 123-134 [DOI: 10.1016/j.rse.2015.04.032http://dx.doi.org/10.1016/j.rse.2015.04.032]
Melgani F and Bruzzone L. 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8): 1778-1790 [DOI: 10.1109/TGRS.2004.831865http://dx.doi.org/10.1109/TGRS.2004.831865]
Mou L C, Lu X Q, Li X L and Zhu X X. 2020. Nonlocal graph convolutional networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(12): 8246-8257 [DOI: 10.1109/TGRS.2020.2973363http://dx.doi.org/10.1109/TGRS.2020.2973363]
Prasad S and Bruce L M. 2008. Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters, 5(4): 625-629 [DOI: 10.1109/LGRS.2008.2001282http://dx.doi.org/10.1109/LGRS.2008.2001282]
Schölkopf B, Smola A and Müller K R. 1997. Kernel principal component analysis//7th International Conference on Artificial Neural Networks. Lausanne: Springer: 583-588 [DOI: 10.1007/BFb0020217http://dx.doi.org/10.1007/BFb0020217]
Shi J B and Malik J. 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8): 888-905 [DOI: 10.1109/34.868688http://dx.doi.org/10.1109/34.868688]
Song X Y, Zhou L L, Li Z G, Chen J, Zeng L and Yan B. 2015. Review on superpixel methods in image segmentation. Journal of Image and Graphics, 20(5): 599-608
宋熙煜, 周利莉, 李中国, 陈健, 曾磊, 闫镔. 2015. 图像分割中的超像素方法研究综述. 中国图象图形学报, 20(5): 599-608 [DOI: 10.11834/jig.20150502http://dx.doi.org/10.11834/jig.20150502]
Vincent L and Soille P. 1991. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6): 583-598 [DOI: 10.1109/34.87344http://dx.doi.org/10.1109/34.87344]
Wei X P, Yu X C, Zhang P Q, Zhi L and Yang F. 2020. CNN with local binary patterns for hyperspectral images classification. Journal of Remote Sensing (Chinese), 24(8): 1000-1009
魏祥坡, 余旭初, 张鹏强, 职露, 杨帆. 2020. 联合局部二值模式的CNN高光谱图像分类. 遥感学报, 24(8): 1000-1009 [DOI: 10.11834/jrs.20208333http://dx.doi.org/10.11834/jrs.20208333]
Xu D, Yang M, Miao Y H, Yan J and Sun M. 2020. Research and application of accurate extraction method of island shoreline based on hyperspectral remote sensing of unmanned aerial vehicle. Marine Sciences, 44(12): 54-60
徐栋, 杨敏, 苗宇宏, 严晋, 孙苗. 2020. 基于无人机高光谱遥感的海岛岸线精准提取方法研究与应用. 海洋科学, 44(12): 54-60 [DOI: 10.11759/hykx20200529002http://dx.doi.org/10.11759/hykx20200529002]
Xu X, Li J, Wu C S and Plaza A. 2018. Regional clustering-based spatial preprocessing for hyperspectral unmixing. Remote Sensing of Environment, 204: 333-346 [DOI: 10.1016/j.rse.2017.10.020http://dx.doi.org/10.1016/j.rse.2017.10.020]
Yan J W, Chen H D and Liu L. 2019. Overview of hyperspectral image classification. Optics and Precision Engineering, 27(3): 680-693
闫敬文, 陈宏达, 刘蕾. 2019. 高光谱图像分类的研究进展. 光学精密工程, 27(3): 680-693 [DOI: 10.3788/OPE.20192703.0680http://dx.doi.org/10.3788/OPE.20192703.0680]
Yu H Y, Xu Z, Wang Y L, Jiao T and Guo Q D. 2021. The use of KPCA over subspaces for cross-scale superpixel based hyperspectral image classification. Remote Sensing Letters, 12(5): 470-477 [DOI: 10.1080/2150704X.2021.1897180http://dx.doi.org/10.1080/2150704X.2021.1897180]
Zhang X D, Wang T J, Zhu S J and Yang Y. 2021. Hyperspectral image classification based on dilated convolutional attention neural network. Acta Optica Sinica, 41(3): 0310001
张祥东, 王腾军, 朱劭俊, 杨耘. 2021. 基于扩张卷积注意力神经网络的高光谱图像分类. 光学学报, 41(3): 0310001 [DOI: 10.3788/AOS202141.0310001http://dx.doi.org/10.3788/AOS202141.0310001]
Zheng C Y, Wang N N and Cui J. 2019. Hyperspectral image classification with small training sample size using superpixel-guided training sample enlargement. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 7307-7316 [DOI: 10.1109/TGRS.2019.2912330http://dx.doi.org/10.1109/TGRS.2019.2912330]
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相关作者
黄媛 湖南师范大学地理科学学院;湖南师范大学地理空间大数据挖掘与应用湖南省重点实验室
贺新光 湖南师范大学地理科学学院;湖南师范大学地理空间大数据挖掘与应用湖南省重点实验室
万义良 湖南师范大学地理科学学院;湖南师范大学地理空间大数据挖掘与应用湖南省重点实验室
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杨凯歌 南京大学地理信息科学系江苏省地理信息技术重点实验室卫星测绘技术与应用国家测绘地理信息局重点实验室
冯学智 南京大学地理信息科学系江苏省地理信息技术重点实验室卫星测绘技术与应用国家测绘地理信息局重点实验室
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相关机构
湖南师范大学地理科学学院
湖南师范大学地理空间大数据挖掘与应用湖南省重点实验室
北方民族大学 计算机科学与工程学院
吕梁学院 计算机科学与技术系
南京大学地理信息科学系江苏省地理信息技术重点实验室卫星测绘技术与应用国家测绘地理信息局重点实验室
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