A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier

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Title: A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier
Authors: Mascolo, Lucio | Lopez-Sanchez, Juan M. | Vicente-Guijalba, Fernando | Nunziata, Ferdinando | Migliaccio, Maurizio | Mazzarella, Giuseppe
Research Group/s: Señales, Sistemas y Telecomunicación
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Agriculture | Classification | Phenology | Polarimetry | Synthetic aperture radar (SAR)
Knowledge Area: Teoría de la Señal y Comunicaciones
Issue Date: Nov-2016
Publisher: IEEE
Citation: IEEE Transactions on Geoscience and Remote Sensing. 2016, 54(11): 6505-6515. doi:10.1109/TGRS.2016.2585744
Abstract: A new methodology to estimate the growth stages of agricultural crops using the time series of polarimetric synthetic aperture radar (PolSAR) images is proposed. The methodology is based on the complex Wishart classifier and both phenological intervals and training areas are identified measuring the distances among polarimetric covariance matrices obtained from the time series of PolSAR data. Consequently, the computation of PolSAR features, which is the main step of state-of-the-art methods, is no longer needed, and the proposed approach can be applied in the same way to any crop type. Experiments undertaken on a dense time series of fully polarimetric C-band RADARSAT-2 images, collected at incidence angles ranging from 23° to 39°, in ascending/descending orbit passes, demonstrate that the proposed methodology can be successfully applied to retrieve the phenological stages of four different crop types. In addition, the effect of combining beams corresponding to different sensor's configurations has been evaluated, showing that it affects the retrieval accuracies. Validation with ground data shows the following: overall accuracy is between 54% and 86%; producer's accuracy (PA) and user's accuracy (UA) range between 21% and 100% and between 33% and 100%, respectively.
Sponsor: This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Projects TEC2011-28201-C02-02 and TIN2014-55413-C2-2-P.
URI: http://hdl.handle.net/10045/58529
ISSN: 0196-2892 (Print) | 1558-0644 (Online)
DOI: 10.1109/TGRS.2016.2585744
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2016 IEEE
Peer Review: si
Publisher version: http://dx.doi.org/10.1109/TGRS.2016.2585744
Appears in Collections:INV - SST - Artículos de Revistas

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