浏览全部资源
扫码关注微信
纸质出版日期： 2021-07-07 ，
扫 描 看 全 文
赵伍迪，李山山，李安，张兵，陈俊.2021.结合深度学习的高光谱与多源遥感数据融合分类.遥感学报，25（7）： 1489-1502
Zhao W D，Li S S，Li A，Zhang B and Chen J. 2021. Deep fusion of hyperspectral images and multi-source remote sensing data for classification with convolutional neural network. National Remote Sensing Bulletin， 25（7）：1489-1502
高光谱数据具有丰富的光谱特征，但是其空间分辨率相对较低。一些遥感数据具有与高光谱数据互补的优势，例如提供更精细的空间信息的高空间分辨率数据和具有高度信息的激光雷达LiDAR（Light Detection and Ranging ）数据。通过将高光谱数据与多源遥感数据进行融合，可以弥补高光谱数据空间分辨率相对较低，空间特征不够丰富的缺点。近年来，基于深度学习的方法已经在遥感数据分类研究中取得了一定的进展。然而，由于深度网络的特征提取过程是一个自主的过程，往往无法精确的获取最有利于遥感数据分类的特征；同时，深度学习方法具有复杂的网络结构和大量的参数，往往会在分类训练过程中造成参数拟合困难。以上这些因素会导致分类效果不佳。针对这些问题，本文提出了一种将卷积神经网络CNN（Convolutional Neural Network）和纹理特征相结合的多源遥感数据特征级融合分类框架。该方法共3个步骤，首先，对高光谱数据或多源遥感数据提取纹理特征；然后，构造CNN，分别将原始高光谱遥感数据、原始多源遥感数据和第一步中获得的纹理特征作为深度网络的输入进行深度特征提取；最后，将分别提取到的深度特征拼接，并利用Softmax分类器进行分类。为了验证本文提出方法的分类效果，本文在休斯顿和塞特福德矿地区公开数据集上进行实验，并将该分类框架与支持向量机分类方法、像素级融合分类方法和特征级融合分类方法进行对比。由此可以分析得出，本文提出的基于深度学习的融合分类方法可以获得较高的分类精度。
Hyperspectral images (HSIs) have abundant spectral characteristics. However
their spatial resolution is relatively low. Some remote sensing data have complementary advantages with HSIs
such as the LiDAR
which can provide elevation information
and the high spatial resolution data
which have precise spatial information. The combination of HSIs with the multi-source remote sensing data for fusion classification can make up the deficiency of tits relatively low spatial resolution. In recent years
deep learning-based methods have been investigated for hyperspectral remote sensing classification and have made breakthroughs. Meanwhile
the feature extraction process of the deep network is an independent process. Therefore
it may not obtain the most beneficial features for classification accurately and may influence the classification accuracy. At the same time
the techniques may not perform well when using limited training samples in HSIs because of massive parameters and complex network structure.
Aiming at this problem
the frequently used traditional features of remote sensing data for classification are discussed in this paper. A new deep learning-based feature level data fusion classification framework that integrates traditional textural features into Convolutional Neural Network (CNN) approach (T-F-CNN) is proposed for the accurate fusion classification of HSIs and multi-source remote sensing data. The proposed method can be implemented in three steps. First
the traditional features are extracted from the HSIs or the multi-source remote sensing data. Second
CNN are built. The original HSIs
the original multi-source remote sensing data
and the traditional features
which are obtained in the first step
are inputted into the CNN of the deep feature extraction. Finally
the deep features obtained in the second step are concatenated in a concatenate layer of CNN
and SoftMax is used to generate classification maps at the end of the framework.Result The proposed classification scheme is tested on two data sets
namely
Houston and Thetford Mines Area data sets. The proposed T-F-CNN is compared with the pixel-level methods
such as Support Vector Machines (SVM) with the Radial Basis Function (RBF) (T-P-SVM)
the CNN fusion method (P-CNN)
and the CNN with traditional features (T-P-CNN); and the feature-level methods
such as CNN fusion method (F-CNN) and CNN method combined with original traditional features (T’-F-CNN). On both data sets
the proposed method shows a higher classification accuracy than other methods. Meanwhile
when the training samples reach a minimum number
the proposed method could provide the highest overall classification accuracy.
The results obtained by the proposed method on the two real hyperspectral data sets demonstrate that the classification accuracy can be improved. Furthermore
the proposed T-F-CNN method outperforms some traditional deep learning methods and exhibits higher computing efficiency than a few advanced deep learning techniques.
卷积神经网络高光谱数据高分辨率数据激光雷达数据图像融合传统特征分类
Convolutional Neural Network (CNN)hyperspectral imageLight Detection and Ranging (LiDAR)high spatial resolution remote sensing datadata fusionGray Level Co-occurrence Matrix (GLCM)classification
Cao Q, Ma A L, Zhong Y F, Zhao J, Zhao B, and Zhang L P. 2019. Urban classification by multi-feature fusion of hyperspectral image and LiDAR data. Journal of Remote Sensing, 23(5):892-903.
曹琼, 马爱龙, 钟燕飞, 赵济, 赵贝, 张良培. 2019. 高光谱-LiDAR多级融合城区地表覆盖分类. 遥感学报, 23(5): 892-903 [DOI:10.11834/jrs.20197512http://dx.doi.org/10.11834/jrs.20197512]
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 [DOI: 10.1109/TGRS.2016.2584107http://dx.doi.org/10.1109/TGRS.2016.2584107]
Chen Y S, Li C Y, Ghamisi P, Jia X P and Gu Y F. 2017. Deep fusion of remote sensing data for accurate classification. IEEE Geoscience and Remote Sensing Letters, 14(8): 1253-1257 [DOI: 10.1109/LGRS.2017.2704625http://dx.doi.org/10.1109/LGRS.2017.2704625]
Cui B G, Wu Y N, Zhong Y, Zhong L W and Lu Y. 2019. Hyperspectral image rolling guidance recursive filtering and classification. Journal of Remote Sensing, 23(3): 431-442
崔宾阁, 吴亚男, 钟勇, 钟利伟, 路燕. 2019. 高光谱图像滚动引导递归滤波与地物分类. 遥感学报, 23(3): 431-442 [DOI:10.11834/jrs.20197510http://dx.doi.org/10.11834/jrs.20197510]
Dai Q L, Luo B, Zhen C, and Wang L G. 2020. Regional multiscale Markov random field for remote sensing image classification. Journal of Remote Sensing, 24(3):245-253
代沁伶, 罗斌, 郑晨, 王雷光. 2020. 区域多尺度马尔可夫随机场的遥感影像分类. 遥感学报, 24(3): 245-253 [DOI: 10.11834/jrs.202018287http://dx.doi.org/10.11834/jrs.202018287]
Dian Y Y, Li Z Y and Pang Y. 2015. Spectral and texture features combined for forest tree species classification with airborne hyperspectral imagery. Journal of the Indian Society of Remote Sensing, 43(1): 101-107 [DOI: 10.1007/s12524-014-0392-6http://dx.doi.org/10.1007/s12524-014-0392-6]
Fauvel M, Benediktsson J A, Chanussot J and Sveinsson J R. 2008. Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles. IEEE Transactions Geoscience and Remote Sensing, 46(11):3804-3814 [DOI:10.1109/TGRS.2008. 922034http://dx.doi.org/10.1109/TGRS.2008.922034]
Feng M B, Liu X and Zhao D. 2014. A fusion method of hyperspectral and multispectral images based on projection and wavelet transformation. Acta Geodaetica et Cartographica Sinica, 43(2): 158-163
丰明博, 刘学, 赵冬. 2014. 多/高光谱遥感图像的投影和小波融合算法. 测绘学报, 43(2): 158-163 [DOI: 10.13485/j.cnki.11-2089.2014.0023http://dx.doi.org/10.13485/j.cnki.11-2089.2014.0023]
Ghamisi P, Benediktsson J, Cavallaro G and Plaza A. 2014. Automatic Framework for Spectral-Spatial Classification based on Supervised Feature Extraction and Morphological Attribute Profiles. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):2147-2160 [DOI:10.1109/JSTARS.2014.2298876http://dx.doi.org/10.1109/JSTARS.2014.2298876]
Gao Q S, Lim S and Jia X P. 2018. Hyperspectral image classification using convolutional neural networks and multiple feature learning. Remote Sensing, 10(2): 299 [DOI: 10.3390/rs10020299http://dx.doi.org/10.3390/rs10020299]
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 [DOI: 10.1155/2015/258619http://dx.doi.org/10.1155/2015/258619]
Imani M and Ghassemian H. 2016. GLCM, Gabor, and morphology profiles fusion for hyperspectral image classification//Proceedings of the 2016 24th Iranian Conference on Electrical Engineering. Shiraz, Iran: IEEE, 460-465 [DOI: 10.1109/IranianCEE.2016.7585566http://dx.doi.org/10.1109/IranianCEE.2016.7585566]
Kaufman J R, Eismann M T and Celenk M. 2015. Assessment of spatial-spectral feature-level fusion for hyperspectral target detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2534-2544 [DOI: 10.1109/jstars.2015.2420651http://dx.doi.org/10.1109/jstars.2015.2420651]
Kumar B and Dikshit O. 2015. Spectral–Spatial Classification of Hyperspectral Imagery Based on Moment Invariants. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6):2457-2463 [DOI:10.1109/ JSTARS. 2015.2446611http://dx.doi.org/10.1109/JSTARS.2015.2446611]
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
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) [DOI: 10.3964/j.issn.1000-0593(201010-2724-05]
Li J, Xi B, Li Y, Du Q and Wang K. 2018. Hyperspectral Classification Based on Texture Feature Enhancement and Deep Belief Networks. Remote Sensing, 10(3):396-416 [DOI: 10.3390/rs10030396http://dx.doi.org/10.3390/rs10030396]
Mahmood Z, Akhter M A, Thoonen G and Scheunders P. 2013. Contextual subpixel mapping of hyperspectral images making use of a high resolution color image. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2): 779-791 [DOI: 10.1109/jstars.2012.2236539http://dx.doi.org/10.1109/jstars.2012.2236539]
Man Q X, Dong P L and Guo H D. 2015. Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification. International Journal of Remote Sensing, 36(6): 1618-1644 [DOI: 10.1080/01431161.2015.1015657http://dx.doi.org/10.1080/01431161.2015.1015657]
Mei A X, Peng W L, Qin Q M and Liu H P. 2001. Beijing: Higher Education Press.
梅安新, 彭望琭, 秦其明, 刘慧平. 2001. 遥感导论. 北京: 高等教育出版社
Merentitis A, Debes C and Heremans R. 2014. Ensemble learning in hyperspectral image classification: toward selecting a favorable bias-variance tradeoff. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1089-1102 [DOI: 10.1109/jstars.2013.2295513http://dx.doi.org/10.1109/jstars.2013.2295513]
Mirzapour F and Ghassemian H. 2015. Improving hyperspectral image classification by combining spectral, texture, and shape features. International Journal of Remote Sensing, 36(4): 1070-1096 [DOI: 10.1080/01431161.2015.1007251http://dx.doi.org/10.1080/01431161.2015.1007251]
Pohl C and van Genderen J L. 1998. Review article multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19(5): 823-854 [DOI: 10.1080/014311698215748http://dx.doi.org/10.1080/014311698215748]
Ruan Q Q. 2001. Digital Image Processing. Beijing: Publishing House of Electronics Industry
阮秋琦. 2001. 数字图像处理学. 北京: 电子工业出版社
Shen L L and Jia S. 2011. Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 49(12): 5039-5046 [DOI: 10.1109/TGRS.2011.2157166http://dx.doi.org/10.1109/TGRS.2011.2157166]
Shen X, Cao L, Xu T and She G H. 2015. Classification of Pinus massoniana and secondary deciduous tree species in northern subtropical region based on high resolution and hyperspectral remotely sensed data. Chinese Journal of Plant Ecology, 39(12): 1125-1135
申鑫, 曹林, 徐婷, 佘光辉. 2015. 基于高分辨率与高光谱遥感影像的北亚热带马尾松及次生落叶树种的分类. 植物生态学报, 39(12): 1125-1135 [DOI: 10.17521/cjpe.2015.0109http://dx.doi.org/10.17521/cjpe.2015.0109]
Tong Q X, Zhang B and Zheng L F. 2006. Hyperspectral Remote Sensing. Beijing: Higher Education Press
童庆禧, 张兵, 郑兰芬. 2006. 高光谱遥感: 原理、技术与应用. 北京: 高等教育出版社
Wang J N, Zheng L F and Tong Q X. 1996. The spectral absorption identification model and mineral mapping by imaging spectrometer data. Remote Sensing of Environment China, 11(1): 20-31
王晋年, 郑兰芬. 童庆禧. 1996. 成象光谱图象光谱吸收鉴别模型与矿物填图研究. 环境遥感, 11(1): 20-31
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, 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]
Wu F Y, Wang X, Ding J W, Du P J, and Tan K. 2020. Improved cascade forest deep learning model for hyperspectral imagery classification. Journal of Remote Sensing, 24(4):439-453
武复宇, 王雪, 丁建伟, 杜培军, 谭琨. 2020. 高光谱遥感影像多级联森林深度网络分类算法. 遥感学报, 24(4): 439-453 [DOI:10.11834/jrs.20209190http://dx.doi.org/10.11834/jrs.20209190]
Yang M D, Huang K S, Yang Y F, Lu L Y, Feng Z Y and Tsai H P. 2016. Hyperspectral image classification using fast and adaptive bidimensional empirical mode decomposition with minimum noise fraction. IEEE Geoscience and Remote Sensing Letters, 13(12): 1950-1954 [DOI: 10.1109/LGRS.2016.2618930http://dx.doi.org/10.1109/LGRS.2016.2618930]
Yue J, Mao S J and Li M. 2016. A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sensing Letters, 7(9): 875-884 [DOI: 10.1080/2150704X.2016.1193793http://dx.doi.org/10.1080/2150704X.2016.1193793]
Zhang B, Li S S, Jia X P, Gao L R and Peng M. 2011. Adaptive markov random field approach for classification of hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 8(5): 973-977 [DOI: 10.1109/LGRS.2011.2145353http://dx.doi.org/10.1109/LGRS.2011.2145353]
Zhao W D, Li S S, Li A, Zhang B and Li Y. 2019. Hyperspectral images classification with convolutional neural network and textural feature using limited training samples. Remote Sensing Letters, 10(5): 449-458 [DOI: 10.1080/2150704X.2019.1569274http://dx.doi.org/10.1080/2150704X.2019.1569274]
Zubko V, Kaufman Y J, Burg R I and Martins J V. 2007. Principal component analysis of remote sensing of aerosols over oceans. IEEE Transactions on Geoscience and Remote Sensing, 45(3): 730-745 [DOI: 10.1109/TGRS.2006.888138http://dx.doi.org/10.1109/TGRS.2006.888138]
相关作者
相关机构
京公网安备11010802024621