Anterior|Siguiente

Retrieval of remote sensing images based on semisupervised deep learning

  • role： First author第一作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

  • Email：zhanghq@radi.ac.cn
  • Introduction：张洪群(1971—　)，男，正高级工程师，研究方向为遥感卫星地面系统相关技术。E-mail：zhanghq@radi.ac.cn
ZHANG Hongqun1,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LIU Xueying12,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, Beijing 100049, China

YANG Sen12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

  • Email：liyu@radi.ac.cn
  • Introduction：李宇(1986—　)，女，助理研究员，研究方向为遥感图像处理。E-mail：liyu@radi.ac.cn
LI Yu1*

resumen

Massive data and diversity characteristics exert higher demands on the retrieval of remote sensing images. The feature extraction algorithm is the most outstanding factor that influences the performance of retrieval methods. Traditional feature extraction methods cause the problem of semantic gap, which occurs when the low-level feature does not perfectly reflect or match the purpose of retrieval. BOVW and multilayer cluster analysis have been proposed to solve semantic gap. However, the usability of these methods is limited given their dependence on classification or cluster algorithms and artificiality. A semi supervised deep-learning method was proposed in this paper. This method combines Sparse Auto encoder (SA) and the principle of Convolutional Neural Networks (CNNs). The method involves four steps: first, the remote sensing images are pretreated with the ZCA whitening method. Second, the feature dictionary is extracted using SA, an algorithm that deals with nonannotated data. Subsequently, the feature dictionary is utilized in image convolution following the principle of CNNs, which imitates the neural net of organisms and decreases the number of features. Average pooling is conducted after image convolution. Both convolution and pooling are implemented to reduce model complexity, thus calculating distance faster. Third, the soft max classifier categorizes remote sensing images into five classes. Lastly, the remote sensing image retrieval is sorted based on the Euclidean distance between the query image and database in the same category as the query image. Experimental results based on high-resolution remote sensing images demonstrate that the proposed method is effective and is more accurate than the methods that are based on color and texture features. Image classification before sorting speeds up retrieval by 27.6%. In addition, the semi-supervised deep learning algorithm is stable when the number of returned images increases. Given that the number of neurons in the hidden SA layer and the region size of pooling primarily affect retrieval results, this study conducted several optimization experiments on these two parameters. Moreover, the algorithm in this research performed well when the number of images in the data set and the retrieval accuracy on a larger data set increased, which are meaningful for the retrieval of massive remote sensing images. The semisupervised deep learning algorithm decreases the time of image annotation, which is an exhausting job. Unlike traditional methods that extracts the features of color, texture, and shape, the method in this study directly extracts the feature dictionary from image elements with good accuracy. Moreover, the efficiency of the proposed method is guaranteed by convolution and pooling, which reduce the feature dimension. Furthermore, our experiment proves that this algorithm performs well in retrieving high-resolution remote sensing images.

palabra clave

remote sensing image retrieval;deep learning;sparse auto encoder;convolutional neural networks;Softmax classifier

References

  1. 1.
    Bengio Y, Lamblin P, Popovici D and Larochelle H. 2006. Greedy layer-wise training of deep networks//Proceedings of the 19th International Conference on Neural Information Processing Systems. Cambridge: MIT Press: 153–160
  2. 2.
    Bishop C M. 2007. Pattern Recognition and Machine Learning. New York: Springer
  3. 3.
    Bourlard H and Kamp Y. 1988. Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics, 59(4/5): 291–294
  4. 4.
    Coates A, Lee H and Ng A Y. 2011. An analysis of single-layer networks in unsupervised feature learning//Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, FL, USA: JMLR: 215–223
  5. 5.
    Duan B B, Han L X and Xie J. 2015. Fuzzy C-means clustering algorithm based on stacked sparse autoencoders. Computer Engineering and Applications, 51(4): 154–157
  6. 6.
    Haralick R M and Shangmugam K. 1973. Texture features for image classification. IEEE Transaction on Systems, SMC-3(6): 768–780 (未找到本条文献信息, 请核对)
  7. 7.
    Hinton G E and Salakhutdinov R R. 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786): 504–507
  8. 8.
    Hu F, Xia G S, Hu J W and Zhang L P. 2015a. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11): 14680–14707
  9. 9.
    Hu F, Xia G S, Hu J W and Zhang L P. 2015b. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11): 14680–14707
  10. 10.
    Huang J, Sun Y and Xu H R. 2013. The application of Sparse coding on image retrieval. Digital Technology and Application, (11): 76–77, 81
  11. 11.
    Jia Y Q, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S and Darrell T. Caffe: convolutional architecture for fast feature embedding. Eprint Arxiv, 2014: 675–678
  12. 12.
    Kitamoto A and Takagi M. 1994. Similarity retrieval of satellite cloud imagery based on optimization principle. IEICE Technical Report Pattern Recognition and Understanding, 94(294): 15–22
  13. 13.
    LeCun Y. 1989. Generalization and network design strategies//Pfeifer R, Schreter Z, Fogelman F and Steels L, eds. Connectionism in Perspective. Zurich, Switzerland: Elsevier: 143–155
  14. 14.
    LeCun Y, Bengio Y and Hinton G. 2015. Deep learning. Nature, 521(7553): 436–444
  15. 15.
    Li D R and Ning X G. 2006. A new image decomposition method for content-based remote sensing image retrieval. Geomatics and Information Science of Wuhan University, 31(8): 659–662
  16. 16.
    Liu M N. 2013. Remote Sensing Image Retrieval Based on Color and Texture. Xian: Xi’an Technological University
  17. 17.
    Liu P Y. 2004. Study on the Algorithms for Content-based Image Feature Extraction. Changchun: Jilin University
  18. 18.
    Lu K, Zhao J D, Ye Y L and Zeng J Z. 2005. Algorithm for semi-supervised learning in image retrieval. Journal of UEST of China, 34(5): 669–671
  19. 19.
    Lu L Z, Liu R Y and Liu N. 2004. Remote sensing image retrieval using color and texture fused features. Journal of Image and Graphics, 9(3): 328–333
  20. 20.
    Luus F P S, Salmon B P, Van den Bergh F and Maharaj B T J. 2015. Multiview deep learning for land-use classification. IEEE Geoscience and Remote Sensing Letters, 12(12): 2448–2452
  21. 21.
    Mnih V and Hintion G E. 2010. Learning to detect roads in high-resolution aerial images//Proceedings of the 11th European Conference on Computer Vision. Berlin Heidelberg: Springer: 210–223 [DOI: 10.1007/978-3-642-15567-3_16]
  22. 22.
    Mojsilovic A and Rogowitz B. 2001. Capturing image semantics with low-level descriptors//Proceedings of the 2001 International Conference on Image Processing. Thessaloniki: IEEE: 18–21 [DOI: 10.1109/ICIP.2001.958942]
  23. 23.
    Sheikholeslami G, Zhang A D and Bian L. 1999. A multi-resolution content-based retrieval approach for geographic images. GeoInformatica, 3(2): 109–139
  24. 24.
    Wang J, Jia Y H and Zhao X. 2014. Tobacco leaf matupity classification based on sparse auto-encoder. Tobacco Science & Technology, 46(9):17-19
  25. 25.
    Yin B C, Wang W T and Wang L C. 2015. Review of deep learning. Journal of Beijing University of Technology, 41(1): 48–59
  26. 26.
    Zhao L J, Tang P and Huo L Z. 2014. Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12): 4620–4631
  27. 27.
    Zhu B, Ramsey M and Chen H. 2000. Creating a large-scale content-based airphoto image digital library. IEEE Transactions on Image Processing, 9(1): 163–167

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website