A Semiautomated Probabilistic Framework for Tree-Cover Delineation From 1-m NAIP Imagery Using a High-Performance Computing Architecture

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 10, 2015, Pages 5690-5708

  Basu, Saikat ^a   Ganguly, Sangram ^b   Nemani, Ramakrishna R ^b   Mukhopadhyay, Supratik ^a   Zhang, Gong ^b   Milesi, Cristina ^b   Michaelis, Andrew ^b   Votava, Petr ^b   Dubayah, Ralph ^c   Duncanson, Laura ^c   Cook, Bruce ^d   Yu, Yifan ^e   Saatchi, Sassan ^f   DiBiano, Robert ^a   Karki, Manohar ^a   Boyda, Edward ^g   Kumar, Uttam ^h   Li, Shuang ^b  

^a Louisiana State University   (United States)

^b NASA AMES RESEARCH CENTER   (United States)

^c UNIVERSITY OF MARYLAND   (United States)

^d NASA GODDARD SPACE FLIGHT CENTER   (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^g SAINT MARY'S COLLEGE OF CALIFORNIA   (United States)

^h OAK RIDGE ASSOCIATED UNIVERSITIES   (United States)

Author keywords

Aerial imagery; conditional random field (CRF); high performance computing (HPC); machine learning; National Agriculture Imagery Program (NAIP); neural network (NN); statistical region merging (SRM)

Indexed keywords

AERIAL PHOTOGRAPHY; AGRICULTURE; ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER ARCHITECTURE; FORESTRY; IMAGE SEGMENTATION; LEARNING SYSTEMS; NETWORK ARCHITECTURE; RANDOM PROCESSES;

AERIAL IMAGERY; CONDITIONAL RANDOM FIELD; HIGH PERFORMANCE COMPUTING (HPC); NATIONAL AGRICULTURE IMAGERY PROGRAM (NAIP); NEURAL NETWORK (NN); STATISTICAL REGION MERGING;

SATELLITE IMAGERY;

ABOVEGROUND BIOMASS; AERIAL PHOTOGRAPHY; ALGORITHM; CANOPY ARCHITECTURE; COMPARATIVE STUDY; DATA SET; GRAPHICAL METHOD; LAND COVER; LIDAR; PIXEL; PROBABILITY; SATELLITE DATA; SATELLITE IMAGERY; URBAN AREA;

AIR CRAFT; COMPUTATION; IMAGES; NEURAL NETWORKS;

CALIFORNIA; UNITED STATES;

EID: 84933564181     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2015.2428197     Document Type: Article

References (52)

1. H. G. Akcay and S. Aksoy, "Automatic detection of geospatial objects using multiple hierarchical segmentations," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2097-2111, Jul. 2008.
2. X. Sun, H. Wang, and K. Fu, "Automatic detection of geospatial objects using taxonomic semantics," IEEE Geosci. Remote Sens. Lett., vol. 7, no. 1, pp. 23-27, Jan. 2010.
3. S. Bhagavathy and B. S. Manjunath, "Modeling and detection of geospatial objects using texture motifs," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 12, pp. 3706-3715, Dec. 2006.
4. D. Guo, H. Xiong, V. Atluri, and N. Adam, "Object discovery in highresolution remote sensing images: A semantic perspective," Knowl. Inf. Syst., vol. 19, no. 2, pp. 211-233, May 2009.
5. A. C. Holt, E. Y.W. Seto, T. Rivard, and P. Gong, "Object-based detection and classification of vehicles from high-resolution aerial photography," Photogramm. Eng. Remote Sens., vol. 75, no. 7, pp. 871-880, 2009.
6. J. Li, R. Nevatia, and S. Nornoha, "User assisted modeling of buildings from aerial images," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 1999, vol. 2, p. 279.
7. E. Sharon, A. Brandt, and R. Basri, "Segmentation and boundary detection using multiscale intensity measurements," in Proc. IEEE CVPR, 2001, pp. 469-476. [Online]. Available: http://dblp.uni-trier.de/db/conf/cvpr/cvpr2001-1.htmlSharonBB01
8. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000.
9. Z. Tu, X. Chen, A. L. Yuille, and S. C. Zhu, "Image parsing: Unifying segmentation, detection, and recognition," in Proc. Int. J. Comput. Vis., 2003, vol. 1, pp. 18-25.
10. D. Nguyen and B. Widrow, "Improving the learning speed of 2-layer neural networks by choosing," in Proc. Int. Joint Conf. Neural Netw. Initial Values Adaptive Weights, 1990, pp. 21-26.
11. C. Leistner, H. Grabner, and H. Bischof, "Semi-supervised boosting using visual similarity learning," in Proc. IEEE Conf. CVPR, Jun. 2008, pp. 1-8.
12. D. S. Chapman, A. Bonn, W. E. Kunin, and S. J. Cornell, "Random forest characterization of upland vegetation and management burning from aerial imagery," J. Biogeogr., vol. 37, no. 1, pp. 37-46, Jan. 2010.
13. P. Gislason, J. Benediktsson, and J. Sveinsson, "Random forest classification of multisource remote sensing and geographic data," in Proc. IEEE IGARSS, Sep. 2004, vol. 2, pp. 1049-1052.
14. P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, "Random forests for land cover classification," Pattern Recog. Lett., vol. 27, no. 4, pp. 294-300, Mar. 2006.
15. R. Archibald and G. Fann, "Feature selection and classification of hyperspectral images with support vector machines," IEEE Geosci. Remote Sens. Lett., vol. 4, no. 4, pp. 674-677, Oct. 2007.
16. M. A. Friedl et al., "Modis collection 5 global land cover: Algorithm refinements and characterization of new datasets," Remote Sens. Environ., vol. 114, no. 1, pp. 168-182, Jan. 2010.
17. J. D. Wickham et al., "Accuracy assessment of NLCD 2006 land cover and impervious surface," Remote Sens. Environ., vol. 130, pp. 294-304, Mar. 2013.
18. D. A. Pouliot, D. J. King, F. W. Bell, and D. G. Pitt, "Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration," Remote Sens. Environ., vol. 82, no. 2/3, pp. 322-334, Oct. 2002.
19. R. Komura, M. Kubo, and K.-I. Muramoto, "Delineation of tree crown in high resolution satellite image using circle expression and watershed algorithm," in Proc. IEEE IGARSS, Sep. 2004, vol. 3, pp. 1577-1580.
20. D. G. Leckie, F. A. Gougeon, N. Walsworth, and D. Paradine, "Stand delineation and composition estimation using semi-automated individual tree crown analysis," Remote Sens. Environ., vol. 85, no. 3, pp. 355-369, May 2003.
21. F. A. Gougeon, "A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images," Can. J. Remote Sens., vol. 21, no. 3, pp. 274-284, 1995.
22. V. Mnih and G. Hinton, "Learning to detect roads in high-resolution aerial images," in Proc. 11th ECCV, Sep. 2010, pp. 210-223.
23. C. Vaduva, I. Gavat, and M. Datcu, "Deep learning in very high resolution remote sensing image information mining communication concept," in Proc. 20th EUSIPCO, Aug. 2012, pp. 2506-2510.
24. G. Liknes, C. Perry, and D. Meneguzzo, "Assessing tree cover in agricultural landscapes using high resolution aerial imagery," J. Terres. Obs, vol. 2, no. 1, pp. 38-55, 2010.
25. WWW2, "Naip," [Online]. Available: http://www.fsa.usda.gov/Internet/FSA-File/naip-2009-info-final.pdf
26. D. AG, 2008. [Online]. Available: http://www.definiens.com
27. WWW3, "Modis," [Online]. Available: http://vip.arizona.edu/documents/MODIS/MODIS-VI-UsersGuide-01-2012.pdf
28. L. Duncanson, B. Cook, G. Hurtt, and R. Dubayah, "An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems," Remote Sens. Environ., vol. 154, pp. 378-386, Nov. 2014.
29. B. D. Cook et al., "NASA goddards LiDAR, hyperspectral and thermal (G-LiHT) airborne imager," Remote Sens., vol. 5, no. 8, pp. 4045-4066, 2013. [Online]. Available: http://www.mdpi.com/2072-4292/5/8/4045
30. R. Nock and F. Nielsen, "Statistical region merging," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1452-1458, Nov. 2004.
31. T.-W. Chen, Y.-L. Chen, and S.-Y. Chien, "Fast image segmentation based on k-means clustering with histograms in HSV color space" in Proc. IEEE Signal Process. Soc. MMSP, 2008, pp. 322-325.
32. R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. Syst., Man Cybern., vol. SMC-3, no. 6, pp. 610-621, Nov. 1973.
33. L. kiat Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 2, pp. 780-795, Mar. 1999.
34. D. A. Clausi, "An analysis of co-occurrence texture statistics as a function of grey level quantization," Can. J. Remote Sens., vol. 28, no. 1, pp. 45-62, Oct. 2002.
35. A. Huete et al., "Overview of the radiometric and biophysical performance of the MODIS vegetation indices," Remote Sens. Environ., vol. 83, no. 12, pp. 195-213, Nov. 2002.
36. J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, "Monitoring vegetation systems in the Great Plains with ERTS," NASA Goddard Space Flight Center 3d ERTS-1 Symp., pp. 309-317, 1974.
37. C. J. Tucker, "Red and photographic infrared linear combinations for monitoring vegetation," Remote Sens. Environ., vol. 8, no. 2, pp. 127-150, May 1979.
38. Y. Kaufman and D. Tanre, "Atmospherically resistant vegetation index (ARVI) for EOS-MODIS," IEEE Trans. Geosci. Remote Sens., vol. 30, no. 2, pp. 261-270, Mar. 1992.
39. Y.-L. Boureau, J. Ponce, and Y. Lecun, "A theoretical analysis of feature pooling in visual recognition," in Proc. 27th Int. Conf. Mach. Learn., Haifa, Isreal, 2010, pp. 1-8.
40. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, ser. Springer Series in Statistics. New York, NY, USA: Springer-Verlag, 2001.
41. G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Improving neural networks by preventing coadaptation of feature detectors," unpublished paper. [Online]. Available: http://arxiv.org/abs/1207.0580
42. C. M. Bishop, Neural Networks for Pattern Recognition. New York, NY, USA: Oxford Univ. Press, 1995.
43. J. D. Lafferty, A. McCallum, and F. C. N. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proc. 8th ICML, San Francisco, CA, USA, 2001, pp. 282-289.
44. G. H. Bakir et al., Predicting Structured Data (Neural Information Processing). Cambridge, MA, USA: MIT Press, 2007.
45. P. Kohli, L. Ladický, and P. H. Torr, "Robust higher order potentials for enforcing label consistency," Int. J. Comput. Vis., vol. 82, no. 3, pp. 302-324, May 2009.
46. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
47. R. E. Schapire, "The strength of weak learnability," Mach. Learn., vol. 5, no. 2, pp. 197-227, Jul. 1990.
48. L. Grady, "Random walks for image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 11, pp. 1768-1783, Nov. 2006.
49. WWW4, "Pleiades," [Online]. Available: https://nex.nasa.gov/nex/static/media/other/Intro-to-Pleiades.pdf
50. J. Vogelmann, T. Sohl, P. Campbell, and D. Shaw, "Regional land cover characterization using Landsat Thematic Mapper Data and Ancillary Data Sources," Environ. Monitoring Assessment, vol. 51, no. 1/2, pp. 415-428, 1998.
51. A. Liaw and M. Weiner, "Classification and regression by random forests," R News, vol. 2, no. 3, pp. 18-22, 2002.
52. WWW5, "R-project," http://www.r-project.org