CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification

Kang ZHANG; Baoqin HEI; Zhuang ZHOU; Shengyang LI

doi:10.11834/jrs.20187075

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CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification
Vol. 22, Issue 1, Pages: 87-96(2018)
Published： 2018-1 ，

Accepted： 30 June 2017
DOI： 10.11834/jrs.20187075

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张康, 黑保琴, 周壮, 李盛阳. 2018. 变异系数降维的CNN高光谱遥感图像分类. 遥感学报, 22(1): 87–96

Zhang K, Hei B Q, Zhou Z and Li S Y. 2018. CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification. Journal of Remote Sensing, 22(1): 87–96
张康, 黑保琴, 周壮, 李盛阳. 2018. 变异系数降维的CNN高光谱遥感图像分类. 遥感学报, 22(1): 87–96 DOI： 10.11834/jrs.20187075.

Zhang K, Hei B Q, Zhou Z and Li S Y. 2018. CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification. Journal of Remote Sensing, 22(1): 87–96 DOI： 10.11834/jrs.20187075.

摘要

为了实现地物精准分类，需要有效地提取与分析高光谱遥感图像中丰富的空—谱信息。提出一种适用于高光谱遥感图像分类的变异系数与卷积神经网络相结合(CV-CNN)的方法。这种新方法引入变异系数的思想来衡量高光谱遥感图像不同波段之间的相似性和差异性，从而提出类间变异系数(CVIE)和类内变异系数(CVIA)的概念。通过计算(CVIE)

/CVIA的值来剔除高光谱遥感图像中的低效波段，然后提取每个像素的空—谱信息，并对其进行2维矩阵化操作，转化为便于卷积神经网络(CNN)输入的灰度图像，最后采用自行构建的适合于高光谱遥感图像分类的CNN模型进行分类。Indian Pines和Pavia University两组数据的实验结果表明，该方法在两种数据集下的总体精度分别达到98.69%和99.66%，有效地改善了高光谱遥感图像的分类精度。

Abstract

Hyperspectral remote Sensing Images (HSIs)

which contain rich spectral and spatial information

are important in the precise classification of earth objects. However

HSIs are usually highly dimensional and non-linear

and they contain large amounts of data. These characteristics increase the difficulty of data processing and bring about the Hughes phenomenon. Conventional methods such as Neural Networks (NN) and support vector machine have solved these problems by reducing the dimensions of HSIs with PCA

ICA

or MNF. Although these methods are effective in the classification of HSIs

they may cause information loss in the original data. Therefore

improving the accuracy of HSI classification with inadequate data is difficult. Recently

deep learning method

especially Convolutional Neural Network (CNN)

has achieved remarkable performance in many fields. Therefore

the application of CNNs to HSI classification shows immense potential. To avoid the Hughes phenomenon and improve the accuracy of his classification

this study proposes a Coefficient of Variation–Convolution Neural Network (CV-CNN) method for the classification of HSIs. After the calculation of the Coefficient of Variation of the IntrA-class (CVIA) and the Coefficient of Variation of the IntEr-class (CVIE) of each band

the bands with low (CVIE)

/CVIA values are excluded. Then

a target pixel with the spectral information of its eight neighbors is organized as multi-layer spectral–spatial information. The spectral–spatial information of the target pixel should then be converted into matrix form. The two-dimensional image suitable for the input of CNN was subsequently obtained. Furthermore

a seven-layer CNN model was constructed with two convolution layers

two max-pooling layers

two full-connection layers

and one softmax layer. Using the seven-layer CNN can effectively improve the accuracy of HSI classification. Experiments were conducted on the Indian Pines dataset and Pavia University dataset to evaluate the performance of the presented CV-CNN method. The results are as follows: (1) Compared with the method that only considers spectral information for HSI classification

the use of spectral–spatial information can actually improve the accuracy of HSI classification. (2) Compared with other CNN methods

the seven-layer CNN model with no band removed can increase the overall accuracy by 2.61% for the Indian Pines dataset and 0.04% for the Pavia University dataset. (3) Based on the same seven-layer model

the experiments show that the classification of HSI with poor bands excluded shows increased accuracy from 97.9% to 98.69% for the Indian Pines dataset and from 99.6% to 99.66% for the Pavia University dataset. This outcome verifies the validity of excluding poor bands on the basis of the CV method. (4) Based on the same seven-layer model

the experiments show that the CV method

compared with the PCA and MNF methods

can increase the overall accuracy by 1.38% and 0.44%

respectively

for the Indian Pines dataset and by 3.26% and 1.83%

respectively

for the Pavia University dataset. The CNN model developed in this study and the method of removing poor bands with the CV technique can improve the accuracy of HSI classification.

关键词

卷积神经网络深度学习变异系数高光谱遥感图像分类

Keywords

Convolutional Neural Networks(CNN)deep learningcoefficient of variationhyperspectral remote sensing imagesclassification

references

Bioucas-Dias J M, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N and Chanussot J. 2013. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 1(2): 6–36 10.1109/MGRS.2013.2244672

Chen Y S, Jiang H L, Li C Y, Jia X P and Ghamisi P. 2016. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 6232–6251 10.1109/TGRS.2016.2584107

Chen Y S, Lin Z H, Zhao X, Wang G and Gu Y F. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2094–2107 10.1109/JSTARS.2014.2329330

Feng J F and Brown D. 1998. Spike output jitter, mean firing time and coefficient of variation. Journal of Physics A: Mathematical and General, 31(4): 1239–1252 10.1088/0305-4470/31/4/013

高红民, 李臣明, 周惠, 张振, 陈玲慧, 何振宇. 2016. 神经网络敏感性分析的高光谱遥感影像降维与分类方法. 电子与信息学报, 38(11): 2715–2723) 10.11999/JEIT160052

Gao H M, Li C M, Zhou H, Zhang Z, Chen L H and He Z Y. 2016. Dimension reduction and classification of hyperspectral remote sensing images based on sensitivity analysis of artificial neural network. Journal of Electronics and Information Technology, 38(11): 2715–2723 (

Gevaert C M, Tang J, García-Haro F J and Kooistra L. 2014. Combining hyperspectral UAV and multispectral formosat-2 imagery for precision agriculture applications//Proceedings of the 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Lausanne, Switzerland: WHISPERS

He K M and Sun J. 2015. Convolutional neural networks at constrained time cost//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA: IEEE: 5353–5360 [DOI: 10.1109/CVPR.2015.7299173]

Hu W, Huang Y Y, Wei L, Zhang F and Li H C. 2015. Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015: 258619 10.1155/2015/258619

Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc.: 1097–1105.

Kuo B C, Ho H H, Li C H, Hung C C and Taur J S. 2014. A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 317–326 10.1109/JSTARS.2013.2262926

Lecun Y, Bottou L, Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278–2324 10.1109/5.726791

Leng J B, Li T, Bai G, Dong Q K and Dong H. 2016. Cube-CNN-SVM: a novel hyperspectral image classification method//Proceedings of the 28th International Conference on Tools with Artificial Intelligence (ICTAI). San Jose, CA: IEEE: 1027–1034 [DOI: 10.1109/ICTAI.2016.0158]

Li H, Xiao G R, Xia T, Tang Y Y and Li L Q. 2014. Hyperspectral image classification using functional data analysis. IEEE Transactions on Cybernetics, 44(9): 1544–1555 10.1109/TCYB.2013.2289331

梁亮, 杨敏华, 李英芳. 2010. 基于ICA与SVM算法的高光谱遥感影像分类. 光谱学与光谱分析, 30(10): 2724–2728) 10.3964/j.issn.1000-0593(2010)10-2724-05

Liang L, Yang M H and Li Y F. 2010. Hyperspectral remote sensing image classification based on ICA and SVM algorithm. Spectroscopy and Spectral Analysis, 30(10): 2724–2728 (

陆应诚, 胡传民, 孙绍杰, 张民伟, 周杨, 石静, 温颜沙. 2016. 海洋溢油与烃渗漏的光学遥感研究进展. 遥感学报, 20(5): 1259–1269) 10.11834/jrs.20166122

Lu Y C, Hu C M, Sun S J, Zhang M W, Zhou Y, Shi J and Wen Y S. 2016. Overview of optical remote sensing of marine oil spills and hydrocarbon seepage. Journal of Remote Sensing, 20(5): 1259–1269 (

Mei S H, Ji J Y, Bi Q Q, Hou J H, Du Q and Li W. 2016. Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification//Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE: 5067–5070 [DOI: 10.1109/IGARSS.2016.7730321]

Roberts D A, Quattrochi D A, Hulley G C, Hook S J and Green R O. 2012. Synergies between VSWIR and TIR data for the urban environment: an evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission. Remote Sensing of Environment, 117: 83–101 10.1016/j.rse.2011.07.021

谭熊, 余旭初, 秦进春, 魏祥坡. 2014. 高光谱影像的多核SVM分类. 仪器仪表学报, 35(2): 405–411

Tan X, Yu X C, Qin J C and Wei X P. 2014. Multiple kernel SVM classification for hyperspectral images. Chinese Journal of Scientific Instrument, 35(2): 405–411 (

田彦平, 陶超, 邹峥嵘, 杨钊霞, 何小飞. 2015. 主动学习与图的半监督相结合的高光谱影像分类. 测绘学报, 44(8): 919–926) 10.11947/j.AGCS.2015.20140221

Tian Y P, Tao C, Zou Z R, Yang Z X and He X F. 2015. Semi-supervised graph-based hyperspectral image classification with active learning. Acta Geodaetica et Cartographica Sinica, 44(8): 919–926 (

Tiwari S K, Saha S K and Kumar S. 2015. Prediction modeling and mapping of soil carbon content using artificial neural network, hyperspectral satellite data and field spectroscopy. Advances in Remote Sensing, 4(1): 63–72 10.4236/ars.2015.41006

张朝阳, 程海峰, 陈朝辉, 郑文伟. 2008. 高光谱遥感的发展及其对军事装备的威胁. 光电技术应用, 23(1): 10–12) 10.3969/j.issn.1673-1255.2008.01.003

Zhang C Y, Cheng H F, Chen Z H and Zheng W W. 2008. The development of hyperspectral remote sensing and its threatening to military equipments. Electro-Optic Technology Application, 23(1): 10–12 (

张成业, 秦其明, 陈理, 王楠, 赵姗姗. 2015. 高光谱遥感岩矿识别的研究进展. 光学精密工程, 23(8): 2407–2418) 10.3788/OPE.20152308.2407

Zhang C Y, Qin Q M, Chen L, Wang N and Zhao S S. 2015. Research and development of mineral identification utilizing hyperspectral remote sensing. Optics and Precision Engineering, 23(8): 2407–2418 (

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