On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/91208
Información del item - Informació de l'item - Item information
Title: On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data
Authors: Xie, Qinghua | Wang, Jinfei | Liao, Chunhua | Shang, Jiali | Lopez-Sanchez, Juan M. | Fu, Haiqiang | Liu, Xiuguo
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: Polarimetric SAR (PolSAR) | Crop classification | Multi-temporal | Target decomposition | Random forest | Cloude–Pottier decomposition | Neumann decomposition | RADARSAT-2
Knowledge Area: Teoría de la Señal y Comunicaciones
Issue Date: 31-Mar-2019
Publisher: MDPI
Citation: Xie Q, Wang J, Liao C, Shang J, Lopez-Sanchez JM, Fu H, Liu X. On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data. Remote Sensing. 2019; 11(7):776. doi:10.3390/rs11070776
Abstract: In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.
Sponsor: This research was funded in part by the Canadian Space Agency SOAR-E program (Grant No. SOAR-E-5489), the National Natural Science Foundation of China (Grant No. 41804004, 41820104005, 41531068), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P.
URI: http://hdl.handle.net/10045/91208
ISSN: 2072-4292
DOI: 10.3390/rs11070776
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: https://doi.org/10.3390/rs11070776
Appears in Collections:INV - SST - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2019_Xie_etal_RemoteSensing.pdf4,44 MBAdobe PDFOpen Preview

This item is licensed under a Creative Commons License Creative Commons