Hidden Markov Models for crop recognition in remote sensing image sequences

Pattern Recognition Letters

Volumn 32, Issue 1, 2011, Pages 19-26

  Leite, Paula Beatriz Cerqueira ^a   Feitosa, Raul Queiroz ^a   Formaggio, Antônio Roberto ^b   Da Costa, Gilson Alexandre Ostwald Pedro ^a   Pakzad, Kian ^c   Sanches, Ieda Del'Arco ^b  

^a PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^b INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

^c LEIBNIZ UNIVERSITY HANNOVER   (Germany)

Author keywords

Crop recognition; Hidden Markov Models; Remote sensing

Indexed keywords

AGRICULTURAL CROPS; ALTERNATIVE APPROACH; CROP CLASS; CROP RECOGNITION; DATA SETS; EXPERIMENTAL ANALYSIS; FIELD WORKS; IMAGE SEGMENTS; LANDSAT IMAGES; PLANT PHENOLOGY; REFERENCE DATA; REMOTE SENSING IMAGES; SEQUENCE OF IMAGES; SOUTHEAST BRAZIL; SPECTRAL PROFILE; SPECTRAL RESPONSE; SPECTRAL VALUE; VISUAL CLASSIFICATION;

CROPS; IMAGE RECONSTRUCTION; IMAGE SEGMENTATION; OBJECT RECOGNITION; REMOTE SENSING;

HIDDEN MARKOV MODELS;

EID: 78049310885     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2010.02.008     Document Type: Conference Paper

References (22)

1. D.E. Ahla, S.T. Gowera, S.N. Burrowsa, N.V. Shabanovb, R.B. Mynenib, and Y. Knyazikhin Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS Remote Sens. Environ. 104 1 2006 88 95
2. Aurdal, L., Huseby, R.B., Vikhamar, D., Eikvil, L., Solberg, A., Solberg, R., 2005. Use of hidden Markov models and phenology for multitemporal satellite image classification: Applications to mountain vegetation classification. In: Third International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multi Temp 2005, pp. 220-224.
3. T. Blaschke, and J. Strobl What's wrong with pixels? Some recent developments interfacing remote sensing and GIS GIS - Zeitschrift für Geoinformationssysteme 14 6 2001 12 17
4. Brovelli, M.A., Crespi, M., Fratarcangeli, F., Giannone, F., Realini, E., 2006. Accuracy assessment of high resolution satellite imagery by leave-one-out method. In: Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, pp. 533-542.
5. H. Bunkle, and T. Caelli Hidden Markov Models - Applications in Computer Vision 2001 World Scientific
6. P.S. Chavez Jr. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data Remote Sens. Environ. 24 9 1988 459 479
7. Gürtler, S., 2003. Estimativa da área agrícola a partir de sensoriamento remoto e banco de pixels amostrais. Master Dissertation, INPE, São José dos Campos, Brazil.
8. B. Jeon, and D.A. Landgrebe Classification with spatio-temporal interpixel class dependency contexts IEEE Trans. Geosci. Remote Sens. 37 3 1992 1227 1233
9. R.A. Johnson, and D.W. Wichern Applied Multivariate Statistical Analysis 1998 Prentice Hall New Jersey
10. Leite, P.B.C., Feitosa, R.Q., Formaggio, A.R., Costa, G.A.O.P., Pakzad, K., Sanches, I.D., 2008. Crop type recognition based on hidden Markov models of plant phenology. In: SIBGRAPI 2008 XXI Brazilian Symposium on Computer Graphics and Image Processing, pp. 12-15.
11. Luiz, A.J.B., Gürtler, S., Gleriani, J.M., Epiphanio, J.C.N., Campos, R.C., 2003. Reflectância a partir do número digital de imagens ETM, XI Simpósio Brasileiro de Sensoriamento Remoto, pp. 2071-2078.
12. P.M. Mather Computer processing of remotely-sensed images: An introduction 1993 John Wiley and Sons Chichester
13. L.R. Rabiner A tutorial on hidden Markov models and selected applications in speech recognition Proc. IEEE 77 2 1989 257 286
14. Reed, B.C., Brown, J.F., 2005. Trend analysis of time-series phenology derived from satellite data. In: Third International Workshop on the Analysis of Multi-Temporal Remote Sensing Images: Multi Temp 2005, pp. 166-168.
15. J. Ren, Z. Chen, Q. Zhou, and H. Tang Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China Internat. J. Appl. Earth Obs. Geoinform. 10 4 2008 403 413
16. Sanches, I.D., 2004. Sensoriamento remoto para o levantamento espectro-temporal e estimativa de áreas de culturas agrícolas. Master Dissertation, INPE, São José dos Campos, Brazil.
17. J.F. Shanahan, J.S. Schepers, D.D. Francis, G.E. Varvel, W.W. Wilhelm, J.M. Tringe, M.R. Schlemmer, and D.J. Major Use of remote-sensing imagery to estimate corn grain yield Agron. J. 93 3 2001 583 589
18. P. Soille Morphological image analysis principles and application second ed. 2003 Springer Verlag Berlin
19. Törmä, M., Rankinen, K., Härmä, P., 2007. Using phenological information derived from MODIS-data to aid nutrient modeling. In: IEEE 2007 International Geoscience and Remote Sensing Symposium, pp. 2298-2301.
20. L. Vincent, and P. Soille Watersheds in digital spaces: An efficient algorithm based on immersion simulations IEEE Trans. Pattern Anal. Machine Intell. 13 6 1991 583 598
21. C.L. Wiegand, A.J. Richardson, and E.T. Kanemasu Leaf area index estimates for wheat from Landsat and their implications for evapotranspiration and crop modeling Agron. J. 71 1979 336 342
22. X. Zhang, M.A. Friedl, C.B. Schaaf, A.H. Strahler, J.C.F. Hodges, F. Gao, B.C. Reed, and A. Huete Monitoring vegetation phenology using MODIS Remote Sens. Environ. 84 2003 471 475