%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi@80/2008/07.17.16.11
%2 sid.inpe.br/sibgrapi@80/2008/07.17.16.11.28
%@doi 10.1109/SIBGRAPI.2008.26
%T Crop type recognition based on Hidden Markov Models of plant phenology
%D 2008
%A Leite, Paula Beatriz Cerqueira,
%A Feitosa, Raul Queiroz,
%A Formaggio, Antônio Roberto,
%A Da Costa, Gilson Alexandre Ostwald Pedro,
%A Pakzad, Kian,
%A Sanches, Ieda Del'Arco,
%@affiliation Catholic University of Rio de Janeiro (PUC-Rio),
%@affiliation Catholic University of Rio de Janeiro (PUC-Rio),
%@affiliation National Institute for Space Research (INPE),
%@affiliation Catholic University of Rio de Janeiro (PUC-Rio),
%@affiliation Leibnitz University Hannover (IPI),
%@affiliation National Institute for Space Research (INPE),
%E Jung, Cláudio Rosito,
%E Walter, Marcelo,
%B Brazilian Symposium on Computer Graphics and Image Processing, 21 (SIBGRAPI)
%C Campo Grande, MS, Brazil
%8 12-15 Oct. 2008
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K crop type recognition, hidden markov models, remote sensing, multitemporal analysis, plant phenology.
%X This work introduces a Hidden Markov Model (HMM) based technique to classify agricultural crops. The method recognizes different crops by analyzing their spectral profiles over a sequence of satellite images. Different HMMs, one for each of the considered crop classes, are used to relate the varying spectral response along the crop cycles with plant phenology. The method assigns for a given image segment the crop class whose corresponding HMM presents the highest probability of emitting the observed sequence of spectral values. Experiments were conducted upon a sequence of 12 previously classified LANDSAT images. The performance of the proposed multitemporal classification method was compared to that of a monotemporal maximum likelihood classifier, and the results indicated a remarkable superiority of the HMM-based method, which achieved an average of no less than 93% accuracy in the identification of the correct crop, for sequences of data containing a single crop class.
%@language en
%3 leite-CropTypeRecognition.pdf