(42-6) 12 * << * >> * Русский * English * Содержание * Все выпуски

Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performanc
Boori M.S., Paringer R., Choudhary K., Kupriyanov A.

Samara National Research University, 443086, Russia, Samara, Moskovskoye Shosse 34,
American Sentinel University, Colorado, USA,
The Hong Kong Polytechnic University, Hong Kong,

IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, 443001, Samara, Russia

 PDF, 3095 kB

DOI: 10.18287/2412-6179-2018-42-6-1035-1045

Страницы: 1035-1045.

Аннотация:
The main aim of this research work is to compare k-nearest neighbor algorithm (KNN) supervised classification with migrating means clustering unsupervised classification (MMC) method on the performance of hyperspectral and multispectral data for spectral land cover classes and develop their spectral library in Samara, Russia. Accuracy assessment of the derived thematic maps was based on the analysis of the classification confusion matrix statistics computed for each classified map, using for consistency the same set of validation points. We were analyzed and compared Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space based sensors that can document detailed information on the distribution of land cover classes, sometimes species level. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). Development of spectral library for land cover classes is a key component needed to facilitate advance analytical techniques to monitor land cover changes. Different land cover classes in Samara were sampled to create a common spectral library for mapping landscape from remotely sensed data.  The development of these libraries provides a physical basis for interpretation that is less subject to conditions of specific data sets, to facilitate a global approach to the application of hyperspectral imagers to mapping landscape. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes’ separation.

Ключевые слова:
hyperspectral; multispectral; satellite data; land cover classification; remote sensing; supervised and unsupervised classification; spectral library.

Цитирование:
Boori MS, Paringer R, Choudhary K, Kupriyanov A. Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performance. Computer Optics 2018; 42(6): 1035-1045. DOI: 10.18287/2412-6179-2018-42-6-1035-1045
.

Литература:

  1. Boori MS, Choudhary K, Paringer RA, Evers M. Food vulnerability analysis in the central dry zone of Myanmar. Computer Optics 2017; 41(4): 552-558. DOI: 10.18287/2412-6179-2017-41-4-552-558.
  2. Chen F, Wang K, Van der Voorde T, Tang TF. Mapping urban land cover from high spatial resolution hyperspectral data: An approach based on simultaneously unmixing similar pixels with jointly sparse spectral mixture analysis. Remote Sensing of Environment 2017; 196: 324-342. DOI: 10.1016/j.rse.2017.05.014.
  3. Boori MS, Choudhary K, Evers M, Paringer R. A review of food security and flood risk dynamics in Central Dry Zone area of Myanmar. Procedia Engineering 2017; 201: 231-238. DOI: 10.1016/j.proeng.2017.09.600.
  4. Dalponte M, Ørka HO, Ene LT, Gobakken T, Næsset E. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sensing of Environment 2014; 140: 306-317. DOI: 10.1016/j.rse.2013.09.006.
  5. Clark ML, Kilham NE. Mapping of land cover in northern California with simulated hyperspectral satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 2016; 119: 228-245. DOI: 10.1016/j.isprsjprs.2016.06.007.
  6. Dudley KL, Dennison PE, Roth KL, Roberts DA, Coates AR. A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients. Remote Sensing of Environment 2015; 167: 121-134. DOI: 10.1016/j.rse.2015.05.004.
  7. Lillesand TM, Kiefer RW. Remote Sensing and Image Interpretation. 4th ed. New York: John Wiley & Sons, Inc; 2000: 363-370. ISBN: 978-0-471-25515-4.
  8. Boori MS, Choudhary K, Kupriyanov A. Vulnerability evaluation from 1995 to 2016 in Central Dry Zone area of Myanmar. International Journal of Engineering Research in Africa 2017; 32: 139-154. DOI:10.4028/www.scientific.net/JERA.32.139.
  9. Camps-Valls G, Tuia D, Bruzzone L, Benediktsson JA. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Processing Magazine 2014; 31(1): 45-54. DOI: 10.1109/MSP.2013.2279179.
  10. Boori MS, Choudhary K, Evers M, Kupriyanov A. Environmental dynamics for Central Dry Zone area of Myanmar. International Journal of Geoinformatics 2017; 13(3):1-12.
  11. Parshakov I, Coburn C, Staenz K. Z-Score distance: A spectral matching technique for automatic class labelling in unsupervised classification. IEEE Geoscience and Remote Sensing Symposium 2014: 1793-1796. DOI: 10.1109/IGARSS.2014.6946801.
  12. Earth Observing 1 (EO-1). Source: <http://eo1.usgs.gov>.
  13. Bioucas-Dias JM, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N, Chanussot J. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine 2013; 1(2): 6-36. DOI: 10.1109/MGRS.2013.2244672.
  14. Datt B, McVicar TR, Van Niel TG, Jupp DLB, Pearlman JS. Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Transaction on Geoscience and Remote Sensing 2003; 41(6): 1246-1259. DOI: 10.1109/TGRS.2003.813206.
  15. Lee JB, Woodyatt AS, Berman M. Enhancement of high spectral resolution remote sensing data by a noise-adjusted principal components transform. IEEE Trans Geosci Remote Sens 1990; 28: 295-304. DOI: 10.1109/36.54356.
  16. Pignatti S., Cavalli R.M., Cuomo V., Fusilli L., Pascucci S., Poscolieri M., Santini F., evaluating hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy. Remote Sensing of Environment 2009; 113(3): 622-634. DOI: 10.1016/j.rse.2008.11.006.
  17. Dalponte M, Ole Ørka H, Ene LT, Gobakken T, Næsset E. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sensing of Environment 2014; 140: 306-317. DOI: 10.1016/j.rse.2013.09.006.
  18. Congalton RG, Green K. Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton, FL: CRC Press; 1999: 137. ISBN: 978-0-87371-986-5.
  19. Underwood EC, Ustin SL, Ramirez CM. A comparison of spatial and spectral image resolution for mapping invasive plants in coastal California. Environmental Management 2007; 39(1): 63-83. DOI: 10.1007/s00267-005-0228-9.
  20. Shepherd KD, Walsh MG. Infrared spectroscopy – enabling an evidence-based diagnostic surveillance approach to agricultural and environmental management in developing countries. Journal of Near Infrared Spectroscopy 2007; 15(1): 1-19. DOI: 10.1255/jnirs.716.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: journal@computeroptics.ru ; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20