Efficiency of the spectral-spatial classification of hyperspectral imaging data

1. V. V. Kozoderov, T. V. Kondranin, and E. V. Dmitriev, “Discrimination of Natural and Man-Made Objects on the Basis of Hyperspectral Images Taken from an Aircraft,” Issled. Zemli iz Kosmosa, No. 1, 35–42 (2014).

   Google Scholar 

2. A. Plaza, Q. Du, J. M. Bioucas-Dias, et al., “Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data,” IEEE Trans. Geosci. Remote Sensing 49 (11), 4103–4110 (2011).

   Article  Google Scholar 

3. A. Plaza, J. A. Benediktsson, J. W. Boardman, et al., “Recent Advances in Techniques for Hyperspectral Image Processing,” Remote Sensing of Environment 113 (1), 110–122 (2009).

   Article  Google Scholar 

4. O. I. Potaturkin, S. M. Borzov, A. O. Potaturkin, and S. B. Uzilov, “Methods and Technologies of Processing of Multispectral and Hyperspectral Data of High-Resolution Remote Sensing,” Vych. Tekhnol. 18 (special issue), 53–60 (2013).

   Google Scholar 

5. X. Huang and L. Zhang, “An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery,” IEEE Trans. Geosci. Remote Sensing 51 (1), 257–272 (2013).

   Article  ADS  Google Scholar 

6. Z. Wang, N. M. Nasrabadi, and T. S. Huang, “Spatial-Spectral Classification of Hyperspectral Images Using Discriminative Dictionary Designed by Learning Vector Quantization,” IEEE Trans. Geosci. Remote Sensing 52 (8), 4808–4822 (2014).

   Article  ADS  Google Scholar 

7. C. Chen, W. Li, E. W. Tramel, et al., “Spectral-Spatial Preprocessing Using Multihypothesis Prediction for Noise-Robust Hyperspectral Image Classification,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7 (4), 1047–1059 (2014).

   Article  Google Scholar 

8. W. Li, Q. Ran, Q. Du, and C. Yang, “Improved Classification of Conservation Tillage Practices Using Hyperspectral Imagery with Spatial-Spectral Features,” in Proc. of the 3rd Intern. Conf. on Agro-geoinformatics, Beijing, China, 2014. DOI: 10.1109/Agro-Geoinformatics.2014.6910589.

   Google Scholar 

9. W. Li, W. Hu, Q. Ran, et al., “Improving Hyperspectral Image Classification Using Smoothing Filter via Sparse Gradient Minimization,” in Proc. of the 8th IAPR Workshop on Pattern Recognition in Remote Sensing, Stockholm, Sweden, 2014. DOI: 10.1109/PRRS.2014.6914279.

   Google Scholar 

10. F. Palsson, M. Ulfarsson, and J. R. Sveinsson, “Hyperspectral Image Denoising Using a Sparse Low Rank Model and Dual-Tree Complex Wavelet Transform,” in Proc. of the IEEE Intern. Conf. on Geoscience and Remote Sensing, Quebec, Canada, 2014, pp. 3670–3673. DOI: 10.1109/IGARSS.2014.6947279.

    Google Scholar 

11. M. Borhani and H. Ghassemian, “Hyperspectral Image Classification Based on Spectral-Spatial Features Using Probabilistic SVM and Locally Weighted Markov Random Fields,” in Proc. of the Iranian Conf. on Intelligent Systems, Bam, Iran, 2014. DOI: 10.1109/IranianCIS.2014.6802573.

    Google Scholar 

12. M. Borhani and H. Ghassemian, “Hyperspectral Image Classification Based on Non-Uniform Spatial-Spectral Kernels,” in Proc. of the Iranian Conf. on Intelligent Systems, Bam, Iran, 2014. DOI: 10.1109/Iranian- CIS.2014.6802579.

    Google Scholar 

13. Z. Ye, M. He, J. E. Fowler, and Q. Du, “Hyperspectral Image Classification Based on Spectra Derivative Features and Locality Preserving Analysis,” in Proc. of the IEEE China Summit & Intern. Conf. on Signal and Information Processing, Xian, China, 2014, pp. 138–142. DOI: 10.1109/ChinaSIP.2014.6889218.

    Google Scholar 

14. V. G. Bondur, “Advanced Approaches to Processing Large Fluxes of Hyperspectral and Multispectral Aerospace Information,” Issled. Zemli iz Kosmosa, No. 1, 4–16 (2014).

    Google Scholar 

15. I. A. Pestunov and S. A. Rylov, “Algorithms of Spectral-Textural Segmentation of Satellite Images with High Spatial Resolution,” Vestn. KemGU 2 (4), 104–109 (2012).

    Google Scholar 

16. A. V. Kuznetsov and V. V. Myasnikov, “Comparison of Algorithms of Controlled Elemental Classification of Hyperspectral Images,” Komp. Optika 38 (3), 494–502 (2014).

    Article  Google Scholar 

17. V. A. Fursov, S. A. Bibikov, and O. A. Baida, “Topical Classification of Hyperspectral Images Based on the Coefficient of Contingency,” Komp. Optika 38 (1), 154–158 (2014).

    Article  Google Scholar 

18. M. Fauvel, Yu. Tarabalka, J. Atli, et al., “Advances in Spectral-Spatial Classification of Hyperspectral Images,” Proc. IEEE 101 (3), 652–675 (2013).

    Article  Google Scholar 

19. Promising Information Technologies of Remote Sensing of the Earth, Ed. by V. A. Soifer (Novaya Tekhnika, Samara, 2015) [in Russian].

20. S. M. Borzov and O. I. Potaturkin, “Classification of Vegetation Types on the Basis of Remote Sensing,” Vestn. NGU, Ser. Inform. Tekhnol. 12 (4), 13–22 (2014).

    Google Scholar 

21. S. M. Borzov, A. O. Potaturkin, O. I. Potaturkin, and A. M. Fedotov, “Analysis of the Efficiency of Classification of Hyperspectral Satellite Images of Natural and Man-Made Areas,” Avtometriya 52 (1), 3–14 (2016) [Optoelectron., Instrum. Data Process. 52 (1), 1–10 (2016)].

    Google Scholar 

22. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, “A Transformation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal,” IEEE Trans. Geosci. Remote Sensing 26 (1), 65–74 (1988).

    Article  ADS  Google Scholar 

23. T. M. Lillesand, R. W. Kiefer, and J. W. Chipman, Remote Sensing and Image Interpretation (John Wiley & Sons, New York, 2004).

    Google Scholar 

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