Time series-based classifier fusion for fine-grained plant species recognition

Pattern Recognition Letters

Volumn 81, Issue , 2016, Pages 101-109

  Faria, Fabio A ^a,c   Almeida, Jurandy ^a,c   Alberton, Bruna ^b   Morellato, Leonor Patricia C ^b   Rocha, Anderson ^c   da S Torres, Ricardo ^c  

^a FEDERAL UNIVERSITY OF SÃO PAULO   (Brazil)

^b SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

^c UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Classifier fusion; Diversity measures; Plant species identification

Indexed keywords

BIOLOGY; CLIMATE CHANGE; DECISION MAKING; GLOBAL WARMING; SEARCH ENGINES; TIME SERIES;

CLASSIFICATION RESULTS; CLASSIFIER FUSION; DIVERSITY MEASURE; ENVIRONMENTAL CHANGE; INNOVATIVE SOLUTIONS; PLANT IDENTIFICATION; PLANT SPECIES IDENTIFICATION; SPECIFIC PROPERTIES;

IMAGE PROCESSING;

EID: 84949485572     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2015.10.016     Document Type: Article

References (34)

1. [1] Ahrends, H., Etzold, S., Kutsch, W., Stoeckli, R., Bruegger, R., Jeanneret, F., Wanner, H., Buchmann, N., Eugster, W., Tree phenology and carbon dioxide fluxes: use of digital photography for process-based interpretation at the ecosystem scale. Clim. Res. 39 (2009), 261–274.
2. [2] Alberton, B., Almeida, J., Henneken, R., da S. Torres, R., Menzel, A., Morellato, L.P.C., Using phenological cameras to track the green up in a Cerrado savanna and its on-the-ground validation. Ecol. Inform. 19 (2014), 62–70.
3. [3] Almeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., da S. Torres, R., Plant species identification with phenological visual rhythms. Proceedings of the IEEE International Conference on eScience (eScience’13), 2013, 148–154.
4. [4] Almeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., da S. Torres, R., Visual rhythm-based time series analysis for phenology studies. Proceedings of the IEEE International Conference on Image Processing (ICIP’13), 2013, 4412–4416.
5. [5] Almeida, J., dos Santos, J.A., Alberton, B., da S. Torres, R., Morellato, L.P.C., Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees. Ecol. Inform. 23 (2014), 49–61.
6. [6] Almeida, J., Santos, J.A., Miranda, W.O., Alberton, B., Morellato, L.P.C., Torres, R.S., Deriving vegetation indices for phenology analysis using genetic programming. Ecol. Inform. 26 (2015), 61–69.
7. [7] Breiman, L., Bagging predictors. Mach. Learn. 24 (1996), 123–140.
8. [8] Chambers, L.E., Altwegg, R., Barbraud, C., Barnard, P., Beaumont, L., Crawford, R., Durant, J.M., Hughes, L., Keatley, M.R., Low, M., Morellato, L.P.C., Poloczanska, E., Ruoppolo, V., Vanstreels, R.E.T., Woehler, E.J., Wolfaardt, A., Changes in southern hemisphere phenology. PlosOne, 8, 2013, e75514.
9. [9] Cheng, H.D., Jiang, X., Sun, Y., Wang, J., Color image segmentation: advances and prospects. Pattern Recognit. 34 (2001), 2259–2281.
10. [10] Cleland, E.E., Chuine, I., Menzel, A., Mooney, H.A., Schwartz, M.D., Shifting plant phenology in response to global change. Trends Ecol. Evol. 22:7 (2007), 357–365.
11. [11] Conti, J.C., Faria, F.A., Almeida, J., Alberton, B., Morellato, L.P.C., Camolesi, L. Jr., da S. Torres, R., Evaluation of time series distance functions in the task of detecting remote phenology patterns. Proceedings of the IEEE International Conference on Pattern Recognition (ICPR’14), 2014, 3126–3131.
12. [12] Faria, F.A., dos Santos, J.A., Rocha, A., da S. Torres, R., A framework for selection and fusion of pattern classifiers in multimedia recognition. Pattern Recognit. Lett. 39 (2014), 52–64.
13. [13] Friedman, J., Hastie, T., Tibshirani, R., The Elements of Statistical Learning, first ed., 2001, Springer.
14. [14] Gée, C., Bossu, J., Jones, G., Truchetet, F., Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agric. 60 (2008), 49–59.
15. [15] Guigues, L., Cocquerez, J., Le Men, H., Scale-sets image analysis. Int. J. Comput. Vis. 68 (2006), 289–317.
16. [16] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The weka data mining software: an update. ACM SIGKDD Explor. Newsl. 11 (2009), 10–18.
17. [17] Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., Franois, C., Ustin, S.L., Prospect + sail models: a review of use for vegetation characterization. Remote Sens. Environ. 113:Suppl. 1 (2009), S56–S66.
18. [18] Jin, H., Eklundh, L., A physically based vegetation index for improved monitoring of plant phenology. Remote Sens. Environ. 152 (2014), 512–525.
19. [19] Kataoka, T., Kaneko, T., Okamoto, H., Hata, S., Crop growth estimation system using machine vision. Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, vol. 2, 2003, b1079–b1083.
20. [20] Kuncheva, L.I., Combining Pattern Classifiers: Methods and Algorithms, 2004, Wiley-Interscience.
21. [21] Kuncheva, L.I., Whitaker, C.J., Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51 (2003), 181–207.
22. [22] Lam, L., Suen, S.Y., Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Trans. Syst., Man, Cybern., Part A: Syst. Hum. 27 (1997), 553–568.
23. [23] Morellato, L.P.C., Rodrigues, R.R., Leit ao Filho, H.F., Joly, C.A., Estudo comparativo da fenologia de espécies arbóreas de floresta de altitude e floresta mesófila semidecídua na serra do iapí, jundiaí, são paulo. Braz. J. Bot. 12 (1989), 85–98.
24. [24] Parry, M., Canziani, O., Palutikof, J., van der Linden, P., Hanson, C., IPCC (2007) Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007, Cambridge UK: Cambridge University Press.
25. [25] Reys, P., Camargo, M.G.G., Teixeira, A.P., Assis, M.A., Grombone-Guaratini, M.T., Morellato, L.P.C., Estrutura e composio florstica entre borda e interior de um cerrado sensu stricto e sua importância para propostas de recuperação. Hoennea 40 (2013), 437–452.
26. [26] Richardson, A.D., Braswell, B.H., Hollinger, D.Y., Jenkins, J.P., Ollinger, S.V., Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 19 (2009), 1417–1428.
27. [27] Richardson, A.D., Jenkins, J.P., Braswell, B.H., Hollinger, D.Y., Ollinger, S.V., Smith, M.L., Use of digital webcam images to track spring greep-up in a deciduous broadleaf forest. Oecologia 152 (2007), 323–334.
28. [28] da S. Torres, R., Hasegawa, M., Tabbone, S., Almeida, J., dos Santos, J.A., Alberton, B., Morellato, L.P.C., Shape-based time series analysis for remote phenology studies. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS’13), 2013, 3598–3601.
29. [29] da S. Torres, R., Medeiros, C.B., Gonçalves, M.A., Fox, E.A., A digital library framework for biodiversity information systems. Int. J. Digit. Libr. 6 (2006), 3–17.
30. [30] Santos, L.C.B., Almeida, J., dos Santos, J.A., Guimar aes, S.J.F., Araújo, A.A., Alberton, B., Morellato, L.P.C., da S. Torres, R., Phenological event detection by visual rhythm dissimilarity analysis. Proceedings of the IEEE International Conference on eScience (eScience’14), 2014, 263–270.
31. [31] Schwartz, M.D., Phenology: An Integrative Environmental Science, 2013, Springer.
32. [32] Sokolova, M., Lapalme, G., A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45 (2009), 427–437.
33. [33] Verstraete, M.M., Radiation transfer in plant canopies: scattering of solar radiation and canopy reflectance. J. Geophys. Res.: Atmos. 93 (1988), 9483–9494.
34. [34] Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A., Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38 (1995), 259–269.