Tree species classification using hyperspectral imagery: A comparison of two classifiers

Remote Sensing

Volumn 8, Issue 6, 2016, Pages

  Ballanti, Laurel ^a   Blesius, Leonhard ^a   Hines, Ellen ^a   Kruse, Bill ^b  

^a SAN FRANCISCO STATE UNIVERSITY   (United States)

^b Kruse Imaging   (United States)

Author keywords

Hyperspectral imagery; Random forest; Support vector machine; Tree species classification

Indexed keywords

COMPUTER INTEGRATED MANUFACTURING; DECISION TREES; FORESTRY; IMAGE SEGMENTATION; REMOTE SENSING; SAMPLING; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

CLASSIFICATION APPROACH; HYPER-SPECTRAL IMAGERIES; INDIVIDUAL PERFORMANCE; NON-PARAMETRIC CLASSIFIERS; OBJECT ORIENTED CLASSIFICATION; PLANNING AND MONITORING; RANDOM FORESTS; TREE SPECIES;

CLASSIFICATION (OF INFORMATION);

EID: 84974801090     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8060445     Document Type: Article

References (55)

1. Leckie, D.G.; Tinis, S.; Nelson, T.; Burnett, C.; Gougeon, F.A.; Cloney, E.; Paradine, D. Issues in species classification of trees in old growth conifer stands. Can. J. Remote Sens. 2005, 31, 175-190.
2. Dalponte, M.; Bruzzone, L.; Gianelle, D. Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Trans. Geosci. Remote Sens. 2008, 46, 1416-1427.
3. Peerbhay, K.Y.; Mutanga, O.; Ismail, R. Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa. ISPRS J. Photogramm. Remote Sens. 2013, 79, 19-28.
4. Ghosh, A.; Fassnacht, F.E.; Joshi, P.K.; Koch, B. A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. Int. J. Appl. Earth Obs. Geoinform. 2014, 26, 49-63.
5. Franklin, S.E. Remote Sensing for Sustainable Forest Management; CRC Press: Boca Raton, FL, USA, 2001.
6. Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction; Cambridge University Press: Cambridge, UK, 2010.
7. Dale, V.; Joyce, L.; McNulty, S.; Neilson, R.; Ayres, M.; Flannigan, M.; Hanson, R.; Irland, L.; Lugo, A.; Peterson, C.; et al. Climate Change and Forest Disturbances. BioScience 2001, 51, 723-734.
8. Landgrebe, D. Hyperspectral Image Data Analysis as a High Dimensional Signal Processing Problem. IEEE Signal Process. Mag. 2002, 19, 17-28.
9. Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random forests for land cover classification. Pattern Recognit. Lett. 2006, 27, 294-300.
10. Van Aardt, J.A.N.; Wynne, R.H. Examining pine spectral separability using hyperspectral data from an airborne sensor: An extension of field-based results. Int. J. Remote Sens. 2007, 28, 431-436.
11. Martin, M.E.; Newman, S.D.; Aber, J.D.; Congalton, R.G. Determining forest species composition using high spectral resolution remote sensing data. Remote Sens. Environ. 1998, 65, 249-254.
12. Clark, M.L.; Roberts, D.A.; Clark, D.B. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens. Environ. 2005, 96, 375-398.
13. Akar, Ö.; Güngör, O. Classification of multispectral images using Random Forest algorithm. J. Geod. Geoinform. 2012, 1, 105-112.
14. Holmgren, J.; Persson, A.; Söderman, U. Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. Int. J. Remote Sens. 2008, 29, 1537-1552.
15. Dalponte, M.; Bruzzone, L.; Gianelle, D. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens. Environ. 2012, 123, 258-270.
16. Key, T.; Warner, T.A.; McGraw, J.B.; Fajvan, M.A. A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest. Remote Sens. Environ. 2001, 75, 100-112.
17. Franklin, S.E.; Hall, R.J.; Moskal, L.M.; Maudie, A.J.; Lavigne, M.B. Incorporating texture into classification of forest species composition from airborne multispectral images. Int. J. Remote Sens. 2000, 21, 61-79.
18. Shippert, P. Why use hyperspectral imagery? Photogramm. Eng. Remote Sens. 2004, 70, 377-396.
19. Heinzel, J.; Koch, B. Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. Int. J. Appl. Earth Obs. Geoinform. 2012, 18, 101-110.
20. Tarabalka, Y.; Chanussot, J.; Benediktsson, J.A. Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit. 2010, 43, 2367-2379.
21. Jones, T.G.; Coops, N.C.; Sharma, T. Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada. Remote Sens. Environ. 2010, 114, 2841-2852.
22. Hughes, G.F. On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inform. Theory 1968, 14, 55-63.
23. Melgani, F.; Bruzzone, L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1778-1790.
24. Rojas, M.; Dópido, I.; Plaza, A.; Gamba, P. Comparison of support vector machine-based processing chains for hyperspectral image classification. Proc. SPIE 2010.
25. Voss, M.; Sugumaran, R. Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object-oriented approach. Sensors 2008, 8, 3020-3036.
26. Buddenbaum, H.; Schlerf, M.; Hill, J. Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. Int. J. Remote Sens. 2005, 26, 5453-5465.
27. Denghui, Z.; Le, Y. Support vector machine based classification for hyperspectral remote sensing images after minimum noise fraction rotation transformation. In Proceedings of the 2011 International Conference on Internet Computing & Information Services (ICICIS), Hong Kong, China, 17-18 September 2011.
28. Exelis Visual Information Solutions. ENVI Help; Exelis Visual Information Solutions: Boulder, CO, USA, 2013.
29. Xiao, Q.; Ustin, S.L.; McPherson, E.G. Using AVIRIS data and multiple-masking techniques to map urban forest tree species. Int. J. Remote Sens. 2004, 25, 5637-5654.
30. Dalponte, M.; Ørka, H.O.; Ene, L.T.; Gobakken, T.; Naesset, E. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens. Environ. 2014, 140, 306-317.
31. Alonzo, M.; Bookhagen, B.; Roberts, D.A. Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sens. Environ. 2014, 148, 70-83.
32. MacLean, M.G.; Congalton, R. Map accuracy assessment issues when using an object-oriented approach. In Proceedings of the American Society for Photogrammetry and Remote Sensing 2012 Annual Conference, Sacramento, CA, USA, 19-23 March 2012.
33. Mountrakis, G.; Im, J.; Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2011, 66, 247-259.
34. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
35. Cutler, D.R.; Edwards, T.C., Jr.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. Random forests for classification in ecology. Ecology 2007, 88, 2783-2792.
36. Schoenherr, A.A. A Natural History of California; University of California Press: Berkeley, CA, USA, 1992.
37. National Parks Conservation Association. State of the Parks, Muir Woods National Monument: A Resource Assessment. 2011. Available online: http://www.npca.org/about-us/center-for-parkresearch/stateoftheparks/muir_woods/MuirWoods.pdf (accessed on 22 October 2014).
38. National Park Service. Annual Park Recreation Visitation (1904-Last Calendar Year) Muir Woods NM (MUWO) Reports. 2014. Available online: https://irma.nps.gov/Stats/Reports/Park/MUWO (accessed on 6 October 2015).
39. National Park Service. Muir Woods: 100th Anniversary 2008; 2008. Available online: http://www.nps.gov/muwo/upload/unigrid_muwo.pdf (accessed on 22 October 2014).
40. Ellen, H. Final Report: Golden Gate LiDAR Project; San Francisco State University: San Francisco, CA, USA, 2011.
41. Stephen, R. GGLP SFSU Hyperspectral Processing Report; University of Victoria-Hyperspectral-LiDAR Research Laboratory: Victoria, BC, Canada, 2013.
42. QCoherent. LP360, Version 2013. Available online: http://www.qcoherent.com/(accessed on 30 April 2013).
43. GRASS Development Team. Geographic Resources Analysis Support System (GRASS) Software, Version 6.4.4. Available online: http://grass.osgeo.org (accessed on 3 May 2015).
44. Trimble. eCognition Developer, Version 9.1. Available online: http://www.ecognition.com (accessed on 12 May 2015).
45. Trimble. eCognition: User Guide 9.1; Trimble: Munich, Germany, 2015.
46. McCoy, R.M. Field Methods in Remote Sensing; Guilford Press: New York City, NY, USA, 2005.
47. Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; CRC Press: Boca Raton, FL, USA, 2008.
48. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2015; Available online: http://www. R-project.org/(accessed on 10 June 2015).
49. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
50. Pal, M.; Foody, G.M. Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 2010, 48, 2297-2307.
51. Foody, G. Thematic map comparison: Evaluating the statistical significance differences in classification accuracy. Photogramm. Eng. Remote Sens. 2004, 70, 627-633.
52. Everitt, B.S. The Analysis of Contingency Tables; Chapman and Hall: London, UK, 1977.
53. Dietterich, T. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998, 10, 1895-1923.
54. Duro, D.C.; Franklin, S.E.; Dubé, M.G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 2012, 118, 259-272.
55. Zhang, C.; Xie, Z. Object-based vegetation mapping in the Kissimmee River watershed using HyMap data and machine learning techniques. Wetlands 2013, 33, 233-244.