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

Remote Sensing

Sofia Siachalou¹  Giorgos Mallinis³  Maria Tsakiri-Strati¹  Tao Cheng²  Zhengwei Yang²  Yoshio Inoue²  Yan Zhu²  Weixing Cao²  Clement Atzberger² 

[1] Laboratory of Photogrammetry and Remote Sensing, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece; E-Mail:;Laboratory of Photogrammetry and Remote Sensing, School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece; E-Mail;Laboratory of Forest Remote Sensing, School of Agricultural and Forestry Sciences, Democritus University of Thrace, Orestiada 68200, Greece; E-Mail:

关键词: crop mapping;    Hidden Markov Models;    time series analysis;    phenology;    multi-sensor;    multi-temporal;    temporal windows;    data fusion;    Mediterranean;   

DOI  :  10.3390/rs70403633

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.