Novosibirsk State University Journal of Information Technologies
Scientic Journal

ISSN 2410-0420 (Online), ISSN 1818-7900 (Print)

Switch to
Russian

All Issues >> Contents: Volume 15, Issue No 4 (2017)

Spectral-Spatial Classification of Vegetative Cover Types Using Hyperspectral Data
Mark Aleksandrovich Guryanov, Sergey Mihaylovich Borzov

Institute of Automation and Electrometry SB RAS
Novosibirsk State University

DOI: DOI 10.25205/1818-7900-2017-15-4-14-21
UDC code: 528.72:004.93

Abstract
The article is devoted to the effectiveness research of controlled spectral-spatial classification of hyperspectral data methods in distinguishing vegetation types (agricultural cropes). A number of approaches to increasing the classification accuracy by considering the pixels’ vicinity on different stages of data processing have been compared using the example of the test image fragment, which was taken during the AVIRIS program. It was shown that method combining spatial processing of initial images with postprocessing of generated classification maps renders to be the most effective.

Key Words
remote sensing, hyperspectral images, surface type classification, spectral and spatial features

How to cite:
Guryanov M. A., Borzov S. M. Spectral-Spatial Classification of Vegetative Cover Types Using Hyperspectral Data // Vestnik NSU Series: Information Technologies. - 2017. - Volume 15, Issue No 4. - P. 14-21. - DOI 10.25205/1818-7900-2017-15-4-14-21. - ISSN 1818-7900. (in Russian).

Full Text in Russian

Available in PDF

References
1. Perspective information technologies of Earth remote sensing: monograph / ed. by V. A. Soyfer. Samara, Novaya tehnika, 2015, 256 p.
2. Chen C., Li W., Tramel E. W., Cui M., Prasad S., Fowler J. E. Spectral-spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2014, 7, 1047–1059.
3. Palsson F., Ulfarsson M. O., Sveinsson J. R. Hyperspectral image denoising using a sparse low rank model and dual-tree complex wavelet transform // Proc. of the Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, 2014, p. 3670–3673.
4. Zhen Ye, Mingyi He, Fowler J. E., Qian Du. Hyperspectral image classification based on spectra derivative features and locality preserving analysis. Proc. of the Signal and Information Processing (ChinaSIP), IEEE China Summit & International Conference, 2014, p. 138–142.
5. Borhani M., Ghassemian H. Hyperspectral Image Classification Based on Spectral-Spatial Features Using Probabilistic SVM and Locally Weighted Markov Random Fields. Iranian Conference on Intelligent Systems (ICIS 2014), 2014, pp. 1–6.
6. Yang Hu, Eli Saber, Sildomar T. Monteiro, Nathan D. Cahill and David W. Messinger. Classification of hyperspectral images based on conditional random fields. Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 940510 (February 27, 2015); doi:10.1117/12.2083374; http://dx.doi.org/10.1117/12.2083374
7. Tarabalka Y., Rana A. Graph-Cut-Based Model for Spectral-Spatial Classification of Hyperspectral Images. International Geoscience and Remote Sensing Symposium (IGARSS); Quebec, Canada. 2014, p. 3418–21.
8. Lillesand M. T., Kiefer R. W., Chipman J. W. Remote Sensing and Image Interpretation. N. Y.: John Wiley & Sons, 2004, 763 p.
9. Hader D. P. Imageanalysis: methods and applications. London: CRC Press, 2000, 480 p.
10. Baumgardner M. F., Biehl L. L., Landgrebe D. A. 220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3. Purdue University Research Repository. 2015. doi:10.4231/R7RX991C.
11. Borzov S. M., Potaturkin A. O., Potaturkin O. I., Fedotov A. M. Study of the classification efficiency of hyperspectral satellite images of natural and anthropogenic territories. Avtometriya, 2016, no. 1, p. 3–14.
12. Green A. A., Berman M., Switzer P., and Craig M. D. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing, 1988, vol. 26, no. 1, p. 65–74.

Publication information
Main title Vestnik NSU Series: Information Technologies, Volume 15, Issue No 4 (2017).
Parallel title: Novosibirsk State University Journal of Information Technologies Volume 15, Issue No 4 (2017).

Key title: Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriâ: Informacionnye tehnologii
Abbreviated key title: Vestn. Novosib. Gos. Univ., Ser.: Inf. Tehnol.
Variant title: Vestnik NGU. Seriâ: Informacionnye tehnologii

Year of Publication: 2017
ISSN: 1818-7900 (Print), ISSN 2410-0420 (Online)
Publisher: Novosibirsk State University Press
DSpace handle


|Home Page| |All Issues| |Information for Authors| |Journal Boards| |Ethical principles| |Editorial Policy| |Contact Information| |Publication fee| |Open Access Policy| |Old Site in Russian|

inftech@vestnik.nsu.ru
© 2006-2018, Novosibirsk State University.