Modeling alternatives for fuzzy Markov chain-based classification of multitemporal remote sensing data

Authors:
Raul Queiroz Feitosa;Gilson Alexandre Ostwald Pedro da Costa;Guilherme Lúcio Abelha Mota;Bruno Feijó
Affiliations:
Pontifical Catholic University of Rio de Janeiro - PUC-Rio, Electrical Engineering Department (DEE), Brazil;Pontifical Catholic University of Rio de Janeiro - PUC-Rio, Electrical Engineering Department (DEE), Brazil;Rio de Janeiro State University - UERJ, Institute of Mathematics and Statistics (IME), Brazil;Pontifical Catholic University of Rio de Janeiro - PUC-Rio, Informatics Department (DI), Brazil
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

In this work, we investigate the application of modeling alternatives regarding fuzzy Markov chain-based, multitemporal, cascade classification of remote sensing data. From a theoretical viewpoint, alternative designs for the fuzzy Markov chain-based model are formally presented. From a pragmatic perspective, experimental results are discussed and analyzed, providing a deeper understanding of the virtues and odds of multitemporal remote sensing data classification based on fuzzy Markov chains. We claim that the key components of the fuzzy Markov chain-based, multitemporal classification model with respect to its alternative designs are the t-norm and s-norm operators, and the fuzzy aggregation function. The main objective of this paper is to investigate how a particular design may affect the classification performance. In addition, this paper aims at assessing the impact of the monotemporal classifiers' accuracies on the quality of the multitemporal classification outcome, according to the selected design alternatives. In conclusion, this paper presents design guidelines for both the developer of image analysis systems and the designer of classification methods based on fuzzy Markov chains.