Phenological visual rhythms: Compact representations for fine-grained plant species identification

Pattern Recognition Letters

Volumn 81, Issue , 2016, Pages 90-100

  Almeida, Jurandy ^a,d   dos Santos, Jefersson A ^b   Alberton, Bruna ^c   Morellato, Leonor Patricia C ^c   da S Torres, Ricardo ^d  

^a FEDERAL UNIVERSITY OF SÃO PAULO   (Brazil)

^b FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^c SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

^d UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Image analysis; Plant identification; Remote phenology; Time series; Visual rhythm

Indexed keywords

BIOLOGY; CLIMATE CHANGE; IMAGE ANALYSIS; LIFE CYCLE; TIME SERIES;

COMPACT REPRESENTATION; COMPARATIVE ANALYSIS; IMAGE DESCRIPTIONS; PHENOLOGICAL OBSERVATIONS; PLANT IDENTIFICATION; PLANT SPECIES IDENTIFICATION; REMOTE PHENOLOGY; VISUAL RHYTHM;

TIME SERIES ANALYSIS;

EID: 84955271439     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2015.11.028     Document Type: Article

References (42)

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. Climate 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 Image Process. (ICIP’13), 2013, 4412–4416.
5. [5] Almeida, J., dos Santos, J.A., Alberton, B., da S. Torres, R., Morellato, L.P.C., Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna. Proceedings of the IEEE International Conference on (eScience’12), 2012, 1–8.
6. [6] 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.
7. [7] 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.
8. [8] Ardila, J.P., Bijker, W., Tolpekin, V.A., Stein, A., Multitemporal change detection of urban trees using localized region-based active contours in vhr images. Remote Sens. Environ. 124 (2012), 413–426.
9. [9] Borda, J.C., Mémoire sur les élections au scrutin. 1781, Histoire de l'Acadmie Royale des Sciences.
10. [10] Brooks, E.B., Thomas, V.A., Wynne, R.H., Coulston, J.W., Fitting the multitemporal curve: A fourier series approach to the missing data problem in remote sensing analysis. IEEE Trans. Geosci. Remote Sens. 50 (2012), 3340–3353.
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] Dierenbach, J., Badeck, F.W., Schaber, J., The plant phenological online database (ppodb): An online database for long-term phenological data. Int. J. Biometeorol. 57 (2013), 805–812.
13. [13] Forster, M., Schmidt, T., Schuster, C., Kleinschmit, B., Multi-temporal detection of grassland vegetation with rapideye imagery and a spectral-temporal library. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS’12), 2012, 4930–4933.
14. [14] Gillespie, A.R., Kahle, A.B., Walker, R.E., Color enhancement of highly correlated images. ii. channel ratio and “chromaticity” transformation techniques. Remote Sens. Environ. 22 (1987), 343–365.
15. [15] Guigues, L., Cocquerez, J., Le Men, H., Scale-sets image analysis. Intl. J. Comput Vis. 68 (2006), 289–317.
16. [16] Henneken, R., Dose, V., Schleip, C., Menzel, A., Detecting plant seasonality from webcams using bayesian multiple change point analysis. Agr. Forest Meteorol. 168 (2013), 177–185.
17. [17] Hmimina, G., Dufrêne, E., Pontailler, J.Y., Delpierre, N., Aubinet, M., Caquet, B., de Grandcourt, A., Burban, B., Flechard, C., Granier, A., Gross, P., Heinesch, B., Longdoz, B., Moureaux, C., Ourcival, J.M., Rambal, S., Andr, L.S., Soudani, K., Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sens. Environ. 132 (2013), 145–158.
18. [18] Huang, J., Kumar, R., Mitra, M., Zhu, W.J., Zabih, R., Image indexing using color correlograms. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’97), 1997, 762–768.
19. [19] Ide, R., Oguma, H., A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems. Ecol. Inform. 16 (2013), 25–34.
20. [20] Jacobs, C.E., Finkelstein, A., Salesin, D., Fast multiresolution image querying. Proceedings of the International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’95), 1995, 277–286.
21. [21] Lee, J.S., Ebrahimi, T., Perceptual video compression: A survey. IEEE J. Sel. Topics Signal Process. 6 (2012), 684–697.
22. [22] Loustau, D., Bosc, A., Colin, A., Davi, H., François, C., Dufrêne, E., Équé, M., Cloppet, E., Arrouays, D., Le Bas, C., Saby, N., Pignard, G., Hamza, N., Granier, A., Breda, N., Ciais, P., Viovy, N., Ogée, J., Delage, J., Modeling the climate change effects on the potential reduction of french plains forests at the sub regional level. Tree Physiol 25 (2005), 813–823.
23. [23] Ma, X., Huete, A., Yu, Q., Coupe, N.R., Davies, K., Broich, M., Ratana, P., Beringer, J., Hutley, L.B., Cleverly, J., Boulain, N., Eamus, D., Spatial patterns and temporal dynamics in savanna vegetation phenology across the north australian tropical transect. Remote Sens. Environ. 139 (2013), 97–115.
24. [24] Negi, G.C.S., Leaf and bud demography and shoot growth in evergreen and deciduous trees of central himalaya, india. Trees 20 (2006), 416–429.
25. [25] Ngo, C.W., Pong, T.C., Chin, R.T., Detection of gradual transitions through temporal slice analysis. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR’99), 1999, 1036–1041.
26. [26] Pass, G., Zabih, R., Miller, J., Comparing images using color coherence vectors. Proceedings of the ACM International Conference on Multimedia (ACM-MM’96), 1996, 65–73.
27. [27] Penatti, O.A.B., Valle, E., da S. Torres, R., Comparative study of global color and texture descriptors for web image retrieval. J. Vis. Commun. Image Represent. 23 (2012), 359–380.
28. [28] Petitjean, F., Kurtz, C., Passat, N., Gançarski, P., Spatio-temporal reasoning for the classification of satellite image time series. Pattern Recognit. Lett. 33 (2012), 1805–1815.
29. [29] 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.
30. [30] 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.
31. [31] Rodrigues, A., Marcal, A.R.S., Cunha, M., Phenology parameter extraction from time-series of satellite vegetation index data using phenosat. Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing Society (IGARSS’12), 2012, 4926–4929.
32. [32] 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 Symposium on Geoscience and Remote Sensing Society (IGARSS’13), 2013, 3598–3601.
33. [33] 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.
34. [34] Schwartz, M.D., Phenology: An Integrative Environmental Science, second, 2013, Springer.
35. [35] Schwartz, M.D., Reed, B.C., White, M.A., Assessing satellite derived start-of-season measures in the coterminous. Int. J. Climatol. 22 (2002), 1793–1805.
36. [36] Stehling, R.O., Nascimento, M.A., Falc ao, A.X., A compact and efficient image retrieval approach based on border/interior pixel classification. Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’02), 2002, 102–109.
37. [37] Stricker, M.A., Orengo, M., Similarity of color images. Proceedings of the SPIE International Conference on Storage and Retrieval for Image and Video Databases, 1995, 381–392.
38. [38] Swain, M.J., Ballard, B.H., Color indexing. Intl J. Comput Vis 7 (1991), 11–32.
39. [39] Woebbecke, D.M., Meyer, G.E., Von-Bargen, K., Mortensen, A.D., Plant species identification, size, and enumeration using machine vision techniques on near-binary images. SPIE Opt. Agr. For. 1836 (1992), 208–219.
40. [40] Woebbecke, D.M., Meyer, G.E., Von-Bargen, K., Mortensen, A.D., Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38 (1995), 259–269.
41. [41] Zezula, P., Amato, G., Dohnal, V., Batko, M., Similarity Search: The Metric Space Approach, first, 2010, Springer Publishing Company, Incorporated.
42. [42] Zhang, D., Lu, G., Shape-based image retrieval using generic fourier descriptor. Signal Process. Image Commun. 17 (2002), 825–848.