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

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.

Research highlights
► Accuracy gain is higher when information from past is more accurate.
► Little or no gain when information from past is not very accurate.
► Found no noteworthy superiority among distinct t-norm/s-norm compositions.
► Aggregation functions that assign different weights to different dates are superior.
► Method was also successful when images from time series were missing.