High Resolution Remote Sensing Image Classification Using Multitask Joint Sparseand Low-rank Representation

LIU Wenxuan; QI Kunlun; WU Baiyan; WU Huayi

doi:10.13203/j.whugis20160044

Volume 43 Issue 2

Feb. 2018

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2018 > 43(2): 297-303. > DOI: 10.13203/j.whugis20160044

LIU Wenxuan, QI Kunlun, WU Baiyan, WU Huayi. High Resolution Remote Sensing Image Classification Using Multitask Joint Sparseand Low-rank Representation[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 297-303. DOI: 10.13203/j.whugis20160044

Citation:

PDF (1115 KB)

High Resolution Remote Sensing Image Classification Using Multitask Joint Sparseand Low-rank Representation

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
School of Architecture and Urban Planning, Hunan University of Science and Technology, Xiangtan 411201, China

Funds:

The Major State Basic Research Development Program of China 2012CB719906

More Information

Author Bio:
LIU Wenxuan, PhD candidate, specializes in machine learning and high-resolution remote-sensing images retrieval. E-mail: liuwenxuan@whu.edu.cn
Corresponding author:
QI Kunlun, PhD. E-mail: qikunlun@whu.edu.cn
Received Date: June 29, 2016
Published Date: February 04, 2018

Graphical Abstract

Abstract

Abstract

Multitask learning is one of the machine learning methods, that trains multiple tasks in parallel using information sharing among the tasks. A high resolution remote sensing images classification model using multitask joint sparse and low-rank representation (MJSLR) is proposed in this paper. The model is a non-smooth convex optimization problem, which contains a convex smooth function and the two convex but non-smooth regularization terms. The accelerated proximal gradient method solves the optimization problem. An experiment is performed with UC Merced Land Use Dataset, with comparisons of accuracies between multitask learning and the single task learning. Experimental results show that the proposed method is competitive with Multiple Kernel Learning(MKL) and the Multitask Joint Sparse Representation(MJSR) methods, which demonstrates the effectiveness of the MJSLR method.
- multitask learning,
- sparse representation,
- low-rank structure,
- remote sensing images,
- image classification

FullText(HTML)

References (27)

References

[1]	Eismann M T, Stocker A D, Nasrabadi N M. Automated Hyperspectral Cueing for Civilian Search and Rescue[J]. Proceedings of IEEE, 2009, 97(6):1031-1055 doi: 10.1109/JPROC.2009.2013561
[2]	GoelP K, PrasherS O, PatelR M, et al. Classification of Hyperspectral Data by Decision Trees and Artificial Neural Networks to Identify Weed Stress and Nitrogen Status of Corn[J]. Computers and Electronics in Agriculture, 2003, 39(2):67-93 doi: 10.1016/S0168-1699(03)00020-6
[3]	Iordache M D, Bioucas-DiasJ M, Plaza A. Sparse Unmixing of Hyperspectral Data[J]. IEEE Transactions Geoscience and Remote Sensing, 2011, 49(6):2014-2039 doi: 10.1109/TGRS.2010.2098413
[4]	Qi K, Wu H, Shen C, et al. Land-Use Scene Classification in High-Resolution Remote Sensing Images Using Improved Correlatons[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12):2403-2407 doi: 10.1109/LGRS.2015.2478966
[5]	张倩, 黄昕, 张良培.多尺度同质区域提取的高分辨率遥感影像分类研究[J].武汉大学学报·信息科学版, 2011, 36(1):117-121 http://ch.whu.edu.cn/CN/abstract/abstract433.shtml Zhang Q, Huang X, Zhang L. Multiscale Images Segmentation and Classification with Supervised ECHO of High Spatial Resolution Remotely Sensed Imagery[J]. Geomatics and Information Science of Wuhan University, 2011, 36(1):117-121 http://ch.whu.edu.cn/CN/abstract/abstract433.shtml
[6]	程诗尧, 梅天灿, 刘国英.顾及结构特征的多层次马尔科夫随机场模型在影像分类中的应用[J].武汉大学学报·信息科学版, 2015, 40(9):1180-1187 http://ch.whu.edu.cn/CN/abstract/abstract3318.shtml Cheng Shiyao, Mei Tiancan, Liu Guoying. Application of Multi-level MRF Using Structural Feature to Remote Sensing Image Classification[J]. Geomatics and Information Science of Wuhan University, 2015, 40(9):1180-1187 http://ch.whu.edu.cn/CN/abstract/abstract3318.shtml
[7]	徐侃, 杨文, 陈丽君, 等.利用主题模型的遥感图像场景分类[J].武汉大学学报·信息科学版, 2011, 36(5):540-543 http://ch.whu.edu.cn/CN/abstract/abstract553.shtml Xu Kan, Yang Wen, Chen Lijun, et al. Satellite Image Scene Categorization Based on Topic Models[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5):540-543 http://ch.whu.edu.cn/CN/abstract/abstract553.shtml
[8]	Caruana R. Multitask Learning[J]. Machine Learning, 1997, 28(1):41-75 doi: 10.1023/A:1007379606734
[9]	浦剑. 多任务学习算法研究[D]. 上海: 复旦大学, 2013 Pu Jian. On Multitask Learning Methods[D]. Shanghai: Fudan University, 2013
[10]	Yuan X, Liu X, Yan S. Visual Classification with Multitask Joint Sparse Representation[J]. IEEE Transactions on Image Processing, 2012, 21(10):4349-4360 doi: 10.1109/TIP.2012.2205006
[11]	Leiva-MurilloJ M, Gómez-Chova L, Camps-Valls G. Multitask Remote Sensing Data Classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 51(1):151-161 doi: 10.1117/12.865045.pdf
[12]	俞斌峰. 多任务学习及其在光谱数据分析中的应用[D]. 合肥: 中国科学技术大学, 2015 Yu Binfeng. Multitask LearningandIts Application in Spectral Multivariate Calibration[D]. Hefei: University of Science and Technology of China, 2015
[13]	Zhang C, Liu J, Tian Q, et al. Image Classification by Non-negative Sparse Coding, Low-rank and Sparse Decomposition[C]. IEEE Computer Vision and Pattern Recognition, Colorado Springs, USA, 2011
[14]	Chen J, Zhou J, Ye J. Integrating Low-rank and Group-sparse Structures for Robust Multi-task Learning[C]. International Conference on Knowledge Discovery and Data Mining, San Diego, USA, 2011
[15]	Zhang T, Ghanem B, Liu B, et al. Low-Rank Sparse Coding for Image Classification[C]. IEEE International Conference on Computer Vision, Sydney, Australia, 2013
[16]	Wright J, Yang A Y, Ganesh A, et al. Robust Face Recognition via Sparse Representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):210-227 doi: 10.1109/TPAMI.2008.79
[17]	Obozinski G, Taskar B, Jordan M. Joint Covariate Selection and Joint Subspace Selection for Multiple Classification Problems[J]. Statistics and Computing, 2009, 20(2):231-252 doi: 10.1007/s11222-008-9111-x
[18]	Lin Y, Liu T, Fuh C. Local Ensemble Kernel Learning for Object Category Recognition[C]. IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007
[19]	Nesterov Y. Introductory Lectures on Convex Optimization:A Basic Course[M]. New York:Kluwer Academic Publishers, 2004
[20]	Beck A, Teboulle M. A Fast Iterative Shrinkage-thresholding Algorithm for Linear Inverse Problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1):183-202 doi: 10.1137/080716542
[21]	Mei S, Cao B, Sun J. Encoding Low-Rank and Sparse Structures Simultaneously in Multi-task Learning[J]. Microsoft Technical Report, 2012, 124(1):1-16
[22]	Ji S, Ye J. An Accelerated Gradient Method for Trace Norm Minimization[C]. The 26th Annual International Conference on Machine Learning, Montreal, Canada, 2009
[23]	Chen X, Pan W, Kwok J T, et al. Accelerated Gradient Method for Multi-task Sparse Learning Problem[C]. The 9th IEEE International Conference on Data Mining, Las Vegas, USA, 2009
[24]	Yang Y, Newsam S. Bag-of-visual-words and Spatial Extensions for Land-use Classification[C]. The 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, USA, 2010
[25]	Berg A C, Malik J. Geometric Blur for Template Matching[C]. IEEE Computer Vision and Pattern Recognition, Hawaii, USA, 2001
[26]	Lazebnik S, Schmid C, Ponce J. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories[C]. IEEE Computer Vision and Pattern Recognition, New York, USA, 2006
[27]	Shechtman E, Irani M. Matching Local Self-similarities Across Images and Videos[C]. IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007

[1]	DONG Yanni, QIN Xuexiang, ZHANG Yuxiang, DU Bo. Balanced Metric Learning in Multilevel Spatial Features for Domain Adaptation in Remote Sensing Image Classification[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240212
[2]	XU Ren, SAIMAITI A-li-mu, LI Er-zhu, WANG Wei. Task-oriented Alignment for Unsupervised Domain Adaptation of Remote sensing scene image classification[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230084
[3]	GUO Congzhou, LI Ke, LI He, TONG Xiaochong, WANG Xiwen. Deep Convolution Neural Network Method for Remote Sensing Image Quality Level Classification[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1279-1286. DOI: 10.13203/j.whugis20200292
[4]	ZHENG Zhaobao, ZHENG Hong. Image Classification Based on Data Gravitation[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11): 1604-1607. DOI: 10.13203/j.whugis20160457
[5]	WU Yiquan, TAO Feixiang, CAO Zhaoqing. Remote Sensing Image Classification Based on Log-Gabor Wavelet and Krawtchouk Moments[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 861-867. DOI: 10.13203/j.whugis20140234
[6]	ZHENG Zhaobao, ZHENG Hong. Fuzzy Image Classification with a Certain Degree[J]. Geomatics and Information Science of Wuhan University, 2016, 41(4): 482-486. DOI: 10.13203/j.whugis20150712
[7]	ZHENG Zhaobao, PAN Li, ZHENG Hong. Application of Image Correlation Degree to Image Fuzzy Classification[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5): 574-577. DOI: 10.13203/j.whugis20140736
[8]	XU Kai, QIN Kun, LIU Xiuguo, LI Dengchao. Pan-concept-level Generation Method and Its Application in Remote Sensing Image Classification[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1078-1082.
[9]	LI Deren, NING Xiaogang. A New Image Decomposition Method for Content-Based Remote Sensing Image Retrieval[J]. Geomatics and Information Science of Wuhan University, 2006, 31(8): 659-662.
[10]	LUO Hongxia, GONG Jianya. Effect Analysis of UFCLS Linear Spectral Mixture Analysis Method on Classification of Remote Sensing Images[J]. Geomatics and Information Science of Wuhan University, 2004, 29(7): 615-618.

Cited By

Cited by

Periodical cited type(0)

Other cited types(3)

Get Citation

PDF

XML

Article views (2956) PDF downloads (569) Cited by(3)

High Resolution Remote Sensing Image Classification Using Multitask Joint Sparseand Low-rank Representation

Abstract

References

Related Articles

Cited by

Periodical cited type(0)

Other cited types(3)

Catalog

Related

High Resolution Remote Sensing Image Classification Using Multitask Joint Sparseand Low-rank Representation

Abstract

References

Related Articles

Cited by

Periodical cited type(0)

Other cited types(3)

Catalog

Related

Export File

Citation

Format

Content