Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (1): 59-66.doi: 10.23940/ijpe.20.01.p7.5966
• Orginal Article • Previous Articles Next Articles
Qinggang Wu*(), Yilan Zhao, Qiuwen Zhang, and Bin Jiang
Submitted on
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Revised on
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Accepted on
Contact:
Qinggang Wu
E-mail:wuqinggang323@126.com
Supported by:
Qinggang Wu, Yilan Zhao, Qiuwen Zhang, and Bin Jiang. Remote Sensing Image Classification based on Fusion of ATLTP and Tamura Texture Features [J]. Int J Performability Eng, 2020, 16(1): 59-66.
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Table 1
Comparison for classification accuracy of individual and fused texture features on UC Merced land-use datasets (two scenes)"
Method | Number of training images | Number of testing image | Number of correct images | Number of wrong images | Accuracy/(%) |
---|---|---|---|---|---|
Tamura | 120 | 60 | 53 | 7 | 88.3 |
ATLTP | 120 | 60 | 59 | 1 | 98.3 |
ATLTP+Tamura | 120 | 60 | 60 | 0 | 100 |
Table 2
Comparison for classification accuracy of individual and fused texture features on NWPU-RESISC45 datasets (two scenes)"
Method | Number of training images | Number of testing images | Number of correct images | Number of wrong images | Accuracy/(%) |
---|---|---|---|---|---|
Tamura | 840 | 420 | 386 | 24 | 91.9 |
ATLTP | 840 | 420 | 410 | 10 | 97.6 |
ATLTP+Tamura | 840 | 420 | 418 | 2 | 99.5 |
1. | F. Yin, Q. Qi,B. Xu, “Building Extraction from High Resolution Remote Sensing Image based on Corner Points,” Geospatial Information, Vol. 16, No. 10, pp. 58-61, October 2018 |
2. | F. Zhang, B. Du,L. Zhang, “Saliency-Guided Unsupervised Feature Learning for Scene Classification,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 4, pp. 2175-2184, March 2015 |
3. | J. Chen, C. Xi, W. Chen, et al., “Research on Spatialization of Buildings in Longnan City, Gansu Province based on Miniature Unmanned Aerial Vehicle and High Resolution Satellite Images,” Journal of Seismological Research, Vol. 41, No. 2, pp. 192-196, April 2018 |
4. | Y Wang, Q. Meng, J. Yang, et al., “Object based Remote Sensing Image Classification based on Feature Selection Method,” Science Technology and Engineering, Vol. 32, No. 10, pp. 107-113, November 2016 |
5. | L. Zhou, D. Hu,Z. Zhou, “Scene Recognition Combining Structural and Textural Features,” Science China Information Sciences,” Vol. 56, No. 7, pp. 1-14, July 2013 |
6. | X. Pan, F. Yang, Y. Yang, et al., “Information Extraction of Residence Area based on Texture Directions and Corner Points,” Science Technology and Engineering, Vol. 31, No. 17, pp. 120-127, November 2017 |
7. | Y. Wang, Q. Meng, J. Yang, et al., “Object based Remote Sensing Image Classification based on Feature Selection Method,” Science Technology and Engineering, Vol. 32, No. 16, pp. 107-113, November 2016 |
8. | W. Tang, “Remote Sensing Water-Land Scenery Classification based on Texture Analysis,” Master's Degree, Huazhong University of Science and Technology, Wu Han, February 2012 |
9. | Z. Li, “Remote Sensing Image Scene Classification based on Local Descriptor and Feature Learning,” Master's degree, Xidian University, Xi An, June 2017 |
10. | H. Cui and K. Huang, “Computer Automatic Classification Method based on Mixed Texture,” Computer Technology and Development, Vol. 28, No. 2, pp. 158-162, November 2018 |
11. | X. Tan and B. Triggs, “Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions,” IEEE Transactions on Image Processing, Vol. 19, No. 6, pp. 1635-1650, January 2010 |
12. | Y. Guo, X. Yin,H. Zhang, “An Improved Local Binary Algorithm for Image Categorization,” Journal of Light Industry, Vol. 32, No. 3, pp. 73-77, May 2017 |
13. | Q. Wu, Y. Zhao, W. Huang,H. Wang, “Remote Sensing Image Classification based on Adaptive Threshold Local Ternary Pattern,” Science Technology and Engineering, Vol. 19, No. 12, pp. 242-247, April 2019 |
14. | H. Tamura, S. Mori,T. Yanawaki, “Texture Features Corresponding to Visual Perception,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 8, No. 6, pp. 460-473, June 1978 |
15. | X. Zhang, P. Shen, J. Gao, et al., “A License Plate Recognition System based on Tamura Texture in Complex Conditions,” inProceedings of IEEE International Conference on Information and Automation (ICIA), pp. 1947-1952, June 2010 |
16. | Y. Wang, “Remote Sensing Image Automatic Classification with Support Vector Machine,” Computer Simulation, Vol. 30, No. 6, pp. 378-381, July 2013 |
17. | Y. Yang and S. Newsam, “Bag-of-Visual-Words and Spatial Extensions for Land-Use Classification,” inProceedings of Sigspatial International Conference on Advances in Geographic Information Systems, pp. 270-279, ACM, New York, November 2010 |
18. | G. Cheng, J. Han,X. Lu, “Remote Sensing Image Scene Classification: Benchmark and State of the Art,” Proceedings of the IEEE, Vol. 105, No. 10, pp. 1865-1883, April 2017 |
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