Username   Password       Forgot your password?  Forgot your username? 

 

Recognition and Classification of High Resolution Remote Sensing Image based on Convolutional Neural Network

Volume 14, Number 11, November 2018, pp. 2852-2863
DOI: 10.23940/ijpe.18.11.p31.28522863

Guanyu Chena,b, Zhihua Caia,b, and Xiang Lia,b

aSchool of Computer Science, China University of Geosciences, Wuhan, 430074, China
bHubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430074, China

(Submitted on August 9, 2018; Revised on September 22, 2018; Accepted on October 7, 2018)

Abstract:

High resolution remote sensing image data is veritable big data. It is not only massive, multi-source, and heterogeneous, but also high-dimensional, multi-scale, and non-stationary. In order to overcome the reduction of classification accuracy and redundancy of spatial data when dealing with high resolution remote sensing images using traditional classification methods, this paper improves the traditional Convolution Neural Network (CNN) from the aspects of both the network structure and the training method, and the improved CNN is used in the classification and recognition of high resolution remote sensing images. The experiments show that the classification accuracy of the improved CNN is better than that of the traditional CNN. Furthermore, the classification accuracy of the improved CNN is better than the Deep Belief Network (DBN), Support Vector Machine (SVM), and traditional BP.

 

References: 25

                  1. Y. Qi and E. Zhu, “A New Fast Matching Algorithm by Trans-Scale Search for Remote Sensing Image,” Chinese Journal of Electronics, Vol. 24, No. 3, pp. 654-660, 2015
                  2. Y. Men, G. Zhang, C. Men, et al., “A Sub-Pixel Disparity Refinement Algorithm based on Lagrange Interpolation,” Chinese Journal of Electronics, Vol. 26, No. 4, pp. 784-789, 2017
                  3. G. Y. Chen, X. Li, and L. L. Wang, “Identification and Classification of Adverse Geological Body based on Convolution Neural Networks,” Geological Science and Technology Information, Vol. 35, No. 1, pp. 205-211, 2016
                  4. W. Jiang, P. Liu, and F. Wen, “Speech Magnitude Spectrum Reconstruction from MFCCs Using Deep Neural Network,” Chinese Journal of Electronics, Vol. 27, No. 2, pp. 393-398, 2018
                  5. G. E. Hinton, S. Osindero, and Y. W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Computation, Vol. 18, No. 7, pp. 1527-1554, 2006
                  6. Y. Bengio, “Learning Deep Architectures for AI,” Foundations & Trends® in Machine Learning, Vol. 2, No. 1, pp. 1-127, 2009
                  7. D. Bai, C. Wang, B. Zhang, et al., “CNN Feature Boosted SeqSLAM for Real-Time Loop Closure Detection,” Chinese Journal of Electronics, Vol. 27, No. 3, pp. 488-499, 2018
                  8. G. Y. Chen, X. Li, and L. Liu, “A Study on the Recognition and Classification Method of High Resolution Remote Sensing Image based on Deep Belief Network,” Bio-Inspired Computing -- Theories and Applications (BICTA), pp. 362-370, Xi’an, China, Oct 2005
                  9. Z. H. Zhao, S. P. Yang, and Z. Q. Ma, “License Plate Character Recognition based on Convolutional Neural Network LeNet-5,” Journal of System Simulation, Vol. 22, No. 3, pp. 638-641, 2010
                  10. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proceedings of International Conference on Neural Information Processing Systems, pp. 1097-1105, Nevada, USA, Dec 2012
                  11. K. Xu, “Study of Convolutional Neural Network Applied on Image Recognition,” Doctoral Dissertation, Zhejiang University, 2012
                  12. G. B. Huang and H. Lee, “Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 157, No. 10, pp. 2518-2525, 2012
                  13. K. He, X. Zhang, S. Ren, et al., “Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,” IEEE Trans Pattern Anal Mach Intell, Vol. 37, No. 9, pp. 1904-1916, 2014
                  14. C. Szegedy, W. Liu, Y. Jia, et al., “Going Deeper with Convolutions,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, Boston, USA, June 2015
                  15. G. Y. Chen, X. Li, and K. An, “Identification and Classification of Remote Sensing Image of Vegetation based on Big Dat,” Geological Science and Technology Information, Vol. 35, No. 3, pp. 204-209, 2016
                  16. P. Li, L. Peng, and J. Wen, “Rejecting Character Recognition Errors Using CNN based Confidence Estimation,” Chinese Journal of Electronics, Vol. 25, No. 3, pp. 520-526, 2016
                  17. S. Ke, Y. Zhao, and B. Li, “Image Retrieval based on Convolutional Neural Network and Kernel based Supervised Hashing,” Chinese Journal of Electronics, Vol. 45, No. 1, pp. 157-163, 2017
                  18. X. R. Zhao, X. Wang, and Q. C. Chen, “Temporally Consistent Depth Map Prediction Using Deep Convolutional Neural Network and Spatial-Temporal Conditional Random Field,” Journal of Computer Science and Technology, Vol. 32, No. 3, pp. 443-456, 2017
                  19. Q. Zhang and L. Zhang, “Convolutional Adaptive Denoising Autoencoders for Hierarchical Feature Extraction,” Frontiers of Computer Science, Vol. 8, No. 1, pp. 1-9, 2018
                  20. A. Zhu and S. Uchida, “Scene Word Recognition from Pieces to Whole,” Frontiers of Computer Science, pp. 1-10, 2018
                  21. Q. S. Zhang and S. C. Zhu, “Visual Interpretability for Deep Learning: A Survey,” Frontiers of Information Technology & Electronic Engineering, Vol. 19, No. 1, pp. 27-39, 2018
                  22. Y. L. Boureau, J. Ponce, and Y. LeCun, “A Theoretical Analysis of Feature Pooling in Visual Recognition,” in Proceedings of the 27th International Conference on Machine Learning, pp. 111-118, Haifa, Israel, June 2010
                  23. D. Scherer, A. Müller, and S. Behnke, “Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition,” Artificial Neural Networks, pp. 92-101, Thessaloniki, Greece, September 2010
                  24. R. Girshick, J. Donahue, T. Darrell, et al., “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, Columbus, USA, Jun 2014
                  25. X. Li and G. Wang, “Optimal Band Selection for Hyperspectral Data with Improved Differential Evolution,” Journal of Ambient Intelligence & Humanized Computing, Vol. 6, No. 5, pp. 675-688, 2015

                                   

                                  Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

                                   
                                  This site uses encryption for transmitting your passwords. ratmilwebsolutions.com