Overview of hyperspectral image classification

Jing-wen YAN; Hong-da CHEN; Lei LIU

doi:10.3788/OPE.20192703.0680

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Overview of hyperspectral image classification

Information Sciences | 更新时间：2020-08-13

- Overview of hyperspectral image classification
- Optics and Precision Engineering Vol. 27, Issue 3, Pages: 680-693(2019)
- 作者机构：
  
  1.汕头大学工学院电子系，广东汕头 515063
  2.汕头大学医学院，广东汕头 515063
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192703.0680
  CLC： TP751;TP181
- Received：30 October 2018，
  
  Accepted：26 November 2018，
  
  Published：15 March 2019
- 稿件说明：
移动端阅览
Jing-wen YAN, Hong-da CHEN, Lei LIU. Overview of hyperspectral image classification[J]. Optics and precision engineering, 2019, 27(3): 680-693.
DOI：

Jing-wen YAN, Hong-da CHEN, Lei LIU. Overview of hyperspectral image classification[J]. Optics and precision engineering, 2019, 27(3): 680-693. DOI： 10.3788/OPE.20192703.0680.

摘要

高光谱图像分类是利用高光谱数据图谱合一且光谱信息丰富的特点，对图像中的每个像素进行分门别类，以达到对地物目标进行高精度分类和自动化识别的目的，是对地观测的重要组成部分。在分析高光谱图像特点的基础上，本文从普通机器学习和深度学习这两方面对高光谱图像像素级分类的研究进展及效果进行总结、评述和比较，通过具体实验的结果对比，直观地展现各种算法的优劣。针对高光谱分类问题，本文从两个方面对今后的研究方向及发展前景进行了分析和展望。一方面，在算法研究上，高光谱图像分类算法可在保证分类精度的前提下降低算法的复杂度，利用多源遥感数据、多特征综合、多尺度复合，提升小样本、少参数分类模型的分类精度，适应智能化、快速化高光谱遥感对地观测的发展要求；另一方面要紧密结合市场应用需求，重视高光谱图像在实际中的应用，研究具有市场竞争力的高效分类算法，提升高光谱图像分类在遥感技术应用领域的竞争力。

Abstract

Hyperspectral image classification comprises the classification of every pixel in an image by applying the combination of hyperspectral data atlas and rich spectral information

which can be employed for achieving high-precision classification and automatic recognition of ground objects. Hyperspectral image classification plays an important role in earth observation. Based on the analysis of the characteristics of hyperspectral images with respect to two aspects of general machine learning and deep learning

the progress in associated research and comparison of the effects of pixel-level classification of hyperspectral images are summarized and discussed in this study. The advantages and disadvantages of various algorithms were visually illustrated by comparing the corresponding results. Research objectives and development prospects of hyperspectral image classification are analyzed with respect to two aspects. Firstly

various algorithms need to be studied. A hyperspectral classification algorithm can guarantee classification accuracy required for reducing the algorithm complexity by incorporating multi-source remote sensing data with multi-feature and multi-scale composites. Such an algorithm can improve the classification accuracy of a small sample of the classification model with few parameters

and it can adapt to the intelligent and rapid development requirements of earth observation. Secondly

market applications need to be closely integrated. Practical applications of hyperspectral images should be considered and efficient classification algorithms with marketable competency should be investigated for enhancing the applicability of hyperspectral image classification in remote sensing applications.

关键词

Keywords

references

黄鸿, 郑新磊.加权空-谱与最近邻分类器相结合的高光谱图像分类[J].光学精密工程, 2016, 24(4):873-881.

HUANG H, ZHENG X L. Hyperspectral image classification based on weighted spatial-spectral and nearest neighbor classifier[J]. Opt. Precision Eng. , 2016, 24(4): 873-881. (in Chinese)

LEE M A, HUANG Y, YAO H, et al .. Determining the effects of storage on cotton and soybean leaf samples for hyperspectral analysis[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014, 7(6):2562-2570.

DALPONTE M, ORKA H O, GOBAKKEN T, et al .. Tree species classification in boreal forests with hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing , 2013, 51(5):2632-2645.

YUAN Y, WANG Q, ZHU G. Fast hyperspectral anomaly detection via High-Order 2-D crossing filter[J]. IEEE Transactions on Geoscience and Remote Sensing , 2014, 53(2):620-630.

ZHANG L, ZHANG L, TAO D, et al .. Hyperspectral remote sensing image subpixel target detection based on supervised metric learning[J]. IEEE Transactions on Geoscience and Remote Sensing , 2014, 52(8):4955-4965.

ZHANG B, WU D, ZHANG L, et al .. Application of hyperspectral remote sensing for environment monitoring in mining areas[J]. Environmental Earth Sciences , 2012, 65(3):649-658.

CAMPS-VALLS G, TUIA D, BRUZZONE L, et al .. Advances in hyperspectral image classification: earth monitoring with statistical learning methods[J]. IEEE Signal Processing Magazine , 2013, 31(1):45-54.

张号逵, 李映, 姜晔楠.深度学习在高光谱图像分类领域的研究现状与展望[J].自动化学报, 2018, 44(6):961-977.

ZHANG H K, LI Y, JIANG Y N. Research status and prospect of in-depth learning in hyperspectral image classification[J]. Acta Automation , 2018, 44 (6). (in Chinese)

CHEN Y, LIN Z, ZHAO X, et al .. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014, 7(6):2094-2107.

侯榜焕, 姚敏立, 贾维敏, 等.空谱结构保持的高光谱图像分类[J].红外与激光工程, 2017, 46(12): 1228001-1228008.

HOU P H, YAO M L, JA W M, et al .. Classification of hyperspectral images with spatial structure preservation[J]. Infrared and Laser Engineering, 2017, 46 (12): 1228001-1228008. (in Chinese)

MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing , 2004, 42(8):1778-1790.

BLANZIERI E, MELGANI F. Nearest neighbor classification of remote sensing images with the maximal margin principle[J]. IEEE Transactions on Geoscience and Remote Sensing , 2008, 46(6):1804-1811.

贺金鑫, 陈圣波, 王阳, 等.一种基于朴素贝叶斯分类模型的高光谱矿物精确识别方法[J].光谱学与光谱分析, 2014, 34(2):505-509.

HE J X, CHEN SH B, WANG Y, et al .. An accurate hyperspectral mineral identification method based on naive bayesian classification model[J]. Spectroscopy and Spectral Analysis , 2014, 34 (2): 505-509. (in Chinese)

华晔, 张涛, 奚后玮, 等.基于决策树的高光谱遥感影像分类方法研究[J].计算机技术与发展, 2012, 22(6):198-202.

HUA Y, ZHANG T, XI H W, et al .. Study on hyperspectral remote sensing image classification method based on decision tree[J]. Computer Technology and Development, 2012, 22 (6): 198-202. (in Chinese)

LI W, CHEN C, SU H, et al .. Local binary patterns and extreme learning machine for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing , 2015, 53(7):3681-3693.

吴银花, 胡炳樑, 高晓惠, 等.利用区域增长技术的自适应高光谱图像分类[J].光学精密工程, 2018, 26(2):426-434.

WU Y H, HU B L, GAO X H, et al .. Adaptive hyperspectral image classification using regional growth technology[J]. Opt. Precision Eng ., 2018, 26 (2): 426-434. (in Chinese)

唐中奇, 付光远, 陈进, 等.基于多尺度分割的高光谱图像稀疏表示与分类[J].光学精密工程, 2015, 23(9):2708-2714.

TANG ZH Q, FU G Y, CHEN J, et al .. Sparse representation and classification of hyperspectral images based on multi-scale segmentation[J]. Opt. Precision Eng ., 2015, 23 (9): 2708-2714. (in Chinese)

CHEN Y, NASRABADI N M, TRAN T D. Hyperspectral image classification using dictionary-based sparse representation[J]. IEEE Transactions on Geoscience & Remote Sensing , 2011, 49(10):3973-3985.

ZHANG H, LI J, HUANG Y, et al .. A nonlocal weighted joint sparse representation classification method for hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2017, 7(6):2056-2065.

FU W, LI S, FANG L, et al .. Hyperspectral image classification via shape-adaptive joint sparse representation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2016, 9(2):556-567.

WANG J, JIAO L, WANG S, et al .. Adaptive nonlocal spatial-spectral kernel for hyperspectral imagery classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2016, 9(9):4086-4101.

GAN L, XIA J, DU P, et al .. Class-oriented weighted kernel sparse representation with region-level kernel for hyperspectral imagery classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018, 11(4): 1118-1130.

KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems. Curran Associates Inc . 2012: 1097-1105.

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]. International Conference on Learning Representations, 2014.

HE K, ZHANG X, REN S, et al .. Deep residual learning for image recognition[J]. Computer Vision and Pattern Recognition , 2016: 770-778.

SZEGEDY C, LIU W, JIA Y, et al .. Going deeper with convolutions[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , 2015.

HU W, HUANG Y, WEI L, et al .. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015(2):1-12.

ROMERO A, GATTA C, CAMPS-VALLS G. Unsupervised deep feature extraction for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3):1349-1362.

ZHAO W, DU S. Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach[J] . IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4544-4554.

LI W, WU G, ZHANG F, et al .. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, PP(99):1-10.

ZHANG L, ZHANG L, DU B. Deep learning for remote sensing data: a technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2):22-40.

ZHANG M, LI W, DU Q. Diverse region-based CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society , 2018, 27(6):2623-2634.

赵洁.基于K均值聚类的高光谱遥感影像分类研究[J].地理空间信息, 2016, 14(3):26-29.

ZHAO J. Classification of hyperspectral remote sensing images based on K-means clustering[J]. Geospatial Information , 2016, 14(3): 26-29. (in Chinese)

舒阳, 李京, 何湜, 等.基于空-谱约束的中餐馆过程混合模型高光谱图像聚类方法[J].光谱学与光谱分析, 2016, 36(4):1158-1162.

SHU Y, LI J, HE SH, et al .. Hyperspectral Image clustering method based on spatial-spectral constrained Chinese restaurant process mixing model[J]. Spectroscopy and Spectral Analysis , 2016, 36 (4): 1158-1162. (in Chinese)

童庆禧, 张兵, 郑兰芬.高光谱遥感——原理、技术与应用[M].北京:中国科学院遥感应用研究所, 2006.

TONG Q X, ZHANG B, ZHENG L F. Hyperspectral Remote Sensing-Principle, Technology and Application [M]. Beijing: Institute of Remote Sensing Applied Research, Chinese Academy of Sciences, 2006. (in Chinese)

杜培军, 夏俊士, 薛朝辉, 等.高光谱遥感影像分类研究进展[J].遥感学报, 2016, 20(2):236-256.

DU P J, XIA J SH, XUE ZH, et al .. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2016, 20(2):236-256. (in Chinese)

侯榜焕, 姚敏立, 贾维敏, 等.面向高光谱图像分类的空谱判别分析[J].光学精密工程, 2018, 26(2):450-460.

HOU P H, YAO M L, JA W M, et al .. Spatial-spectral discriminant analysis for hyperspectral image classification[J]. Opt. Precision Eng., 2018, 26(2): 450-460. (in Chinese)

CAMPS-VALLS G, GOMEZ-CHOVA L, MUNOZ-MARI J, et al .. Composite kernels for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters , 2006, 3(1):93-97.

FAUVEL M, TARABALKA Y, BENEDIKTSSON J A, et al .. Advances in spectral-spatial classification of hyperspectral images[J]. Proceedings of the IEEE , 2013, 101(3):652-675.

LI J, MARPU P R, PLAZA A, et al .. Generalized composite kernel framework for hyperspectral Image classification[J]. IEEE Transactions on Geoscience and Remote Sensing , 2013, 51(9):4816-4829.

GHAMISI P, BENEDIKTSSON J A, ULFARSSON M O. Spectral-spatial classification of hyperspectral images based on hidden markov random fields[J]. IEEE Transactions on Geoscience and Remote Sensing , 2014, 52(5):2565-2574.

HINTON G E, OSINDERO S, TEH Y W, et al .. A fast learning algorithm for deep belief nets[J]. Neural Computation , 2006, 18(7): 1527-1554.

HINTON G E, SALAKHUTDINOV R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.

MA X, WANG H, GENG J, et al .. Spectral-spatial classification of hyperspectral image based on deep auto-encoder[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2016, 9(9): 4073-4085.

LIU Y, CAO G, SUN Q, et al .. Hyperspectral classification via deep networks and superpixel segmentation[J]. Journal of Remote Sensing , 2015, 36(13): 3459-3482.

CHEN Y, ZHAO X, JIA X, et al .. Spectral-spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2015, 8(6): 2381-2392.

ZHONG P, GONG Z, LI S, et al .. Learning to diversify deep belief networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing , 2017, 55(6): 3516-3530.

HU W, HUANG Y, WEI L, et al .. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 2015(2):1-12.

MEI S, JI J, BI Q, et al .. Integrating spectral and spatial Information into deep convolutional neural networks for hyperspectral classification[C]. Geoscience and Remote Sensing Symposium. IEEE, 2016: 5067-5070.

MAKANTASIS K, KARANTZALOS K, DOULAMIS A, et al .. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]. Geoscience and Remote Sensing Symposium. IEEE , 2015: 4959-4962.

YUE J, ZHAO W, MAO S, et al .. Spectral-spatial classification of hyperspectral images using deep convolutional neural networks[J]. Remote Sensing Letters, 2015, 6(6):468-477.

YANG J, ZHAO Y, CHAN C W, et al .. Hyperspectral image classification using two-channel deep convolutional neural network[C]. Geoscience and Remote Sensing Symposium. IEEE, 2016: 5079-5082.

XU X, LI W, RAN Q, et al .. Multisource remote sensing data classification based on convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, PP(99):1-13.

LI Y, ZHANG H, SHEN Q. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network[J]. Remote Sensing , 2017, 9(1):67.

ZHANG M, LI W, DU Q. Diverse region-based CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society , 2018, 27(6):2623.

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