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Remote Sens. 2015, 7(4), 3633-3650; doi:10.3390/rs70403633

A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data

1
Laboratory of Photogrammetry and Remote Sensing, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
2
Laboratory of Forest Remote Sensing, School of Agricultural and Forestry Sciences, Democritus University of Thrace, Orestiada 68200, Greece
*
Author to whom correspondence should be addressed.
Academic Editors: Tao Cheng, Zhengwei Yang, Yoshio Inoue, Yan Zhu, Weixing Cao, Clement Atzberger and Prasad S. Thenkabail
Received: 14 January 2015 / Revised: 13 March 2015 / Accepted: 23 March 2015 / Published: 26 March 2015
(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)
View Full-Text   |   Download PDF [18784 KB, uploaded 26 March 2015]   |  

Abstract

Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden Markov Models in crop classification, based on the time-series analysis of phenological states, inferred by a sequence of remote sensing observations. More specifically, we model the dynamics of vegetation over an agricultural area of Greece, characterized by spatio-temporal heterogeneity and small-sized fields, using RapidEye and Landsat ETM+ imagery. In addition, the classification performance of image sequences with variable spatial and temporal characteristics is evaluated and compared. The classification model considering one RapidEye and four pan-sharpened Landsat ETM+ images was found superior, resulting in a conditional kappa from 0.77 to 0.94 per class and an overall accuracy of 89.7%. The results highlight the potential of the method for operational crop mapping in Euro-Mediterranean areas and provide some hints for optimal image acquisition windows regarding major crop types in Greece. View Full-Text
Keywords: crop mapping; Hidden Markov Models; time series analysis; phenology; multi-sensor; multi-temporal; temporal windows; data fusion; Mediterranean crop mapping; Hidden Markov Models; time series analysis; phenology; multi-sensor; multi-temporal; temporal windows; data fusion; Mediterranean
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Siachalou, S.; Mallinis, G.; Tsakiri-Strati, M. A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data. Remote Sens. 2015, 7, 3633-3650.

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