NDLI: Crop Type Recognition Based on Hidden Markov Models of Plant Phenology

Content Provider	IEEE Xplore Digital Library
Author	Leite, P. Feitosa, R.Q. Formaggio, A.R. Costa, G. Pakzad, K. Sanches, I.
Copyright Year	2008
Description	Author affiliation: Leibnitz Univ., Hannover (Sanches, I.) \|\| Catholic Univ. of Rio de Janeiro, Rio de Janeiro (Leite, P.; Feitosa, R.Q.; Costa, G.; Pakzad, K.) \|\| Nat. Inst. for Space Res., Sao Jose dos Campos (Formaggio, A.R.)
Abstract	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.
Starting Page	27
Ending Page	34
File Size	489964
Page Count	8
File Format	PDF
ISBN	9780769533582
ISSN	15301834
DOI	10.1109/SIBGRAPI.2008.26
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-12
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Agriculture Hidden Markov models Image segmentation Satellites Soil Classification algorithms Training Plant phenology Crop type recognition Hidden Markov Models Remote sensing Multitemporal analysis
Content Type	Text
Resource Type	Article

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