DSpace Colección:
http://hdl.handle.net/10045/22816
2019-05-24T08:25:48ZOn the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data
http://hdl.handle.net/10045/91208
Título: On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data
Autor/es: Xie, Qinghua; Wang, Jinfei; Liao, Chunhua; Shang, Jiali; Lopez-Sanchez, Juan M.; Fu, Haiqiang; Liu, Xiuguo
Resumen: 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.2019-03-31T00:00:00ZInfluence of Incidence Angle in the Correlation of C-band Polarimetric Parameters with Biophysical Variables of Rain-fed Crops
http://hdl.handle.net/10045/89550
Título: Influence of Incidence Angle in the Correlation of C-band Polarimetric Parameters with Biophysical Variables of Rain-fed Crops
Autor/es: Valcarce-Diñeiro, Rubén; Lopez-Sanchez, Juan M.; Sánchez, Nilda; Arias-Pérez, Benjamín; Martínez-Fernández, José
Resumen: A multi-temporal field experiment was conducted within the Soil Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain in order to retrieve useful crop information. The objective of this research was to evaluate the potential of polarimetric observations for crop monitoring by exploiting a time series of 20 quad-pol RADARSAT-2 images at different incidence angles (i.e. 25°, 31°, and 36°) during an entire growing season of rain-fed crops, from February to July 2015. The time evolution of 6 crop biophysical variables was gathered from the field measurements, whereas 10 polarimetric parameters were derived from the images. Thus, a subsequent correlation analysis between both datasets was performed. The study demonstrates that the backscattering ratios (HH/VV and HV/VV), the normalized correlation between HH and VV (γHHVV), and the dominant alpha angle (α1), showed significant and relevant correlations with several biophysical variables such as biomass, height, or leaf area index (LAI) at incidence angles of 31° or 36°. The joint use of data acquired with different beams could be exploited effectively to increase the refresh rate of information about crop condition with respect to a single incidence acquisition scheme.2019-03-08T00:00:00ZSelection of PolSAR Observables for Crop Biophysical Variable Estimation With Global Sensitivity Analysis
http://hdl.handle.net/10045/88208
Título: Selection of PolSAR Observables for Crop Biophysical Variable Estimation With Global Sensitivity Analysis
Autor/es: Erten, Esra; Taşkın, Gülşen; Lopez-Sanchez, Juan M.
Resumen: The role of global sensitivity analysis (GSA) is to quantify and rank the most influential features for biophysical variable estimation. In this letter, an approximation model, called high-dimensional model representation (HDMR), is utilized to develop a regression method in conjunction with a GSA in the context of determining key input drivers in the estimation of crop biophysical variables from polarimetric synthetic aperture radar data. A multitemporal Radarsat-2 data set is used for the retrieval of three biophysical variables of barley: leaf area index, normalized difference vegetation index, and Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie stage. The HDMR technique is first adopted to estimate a regression model with all available polarimetric features for each biophysical parameter, and sensitivity indices of each feature are then derived to explain the original space with a smaller number of features in which a final regression model is established. To evaluate the applicability of this methodology, root-mean square and coefficient of determination were performed under different amounts of samples. Results highlight that HDMR can be used effectively in biophysical variable estimation for not only reducing computational cost but also for providing a robust regression.2019-02-01T00:00:00ZEfficient type-4 and type-5 non-uniform FFT methods in the one-dimensional case
http://hdl.handle.net/10045/84077
Título: Efficient type-4 and type-5 non-uniform FFT methods in the one-dimensional case
Autor/es: Selva, Jesus
Resumen: The so-called non-uniform fast Fourier transform (NFFT) is a family of algorithms for efficiently computing the Fourier transform of finite-length signals, whenever the time or frequency grid is non-uniformly spaced. Among the five usual NFFT types, types 4 and 5 involve an inversion problem, and this makes them the most intensive computationally. The usual efficient methods for these last types are either based on a fast multipole (FM) or on an iterative conjugate gradient (CG) method. The purpose of this study is to present efficient methods for these type-4 and type-5 NFFTs in the one-dimensional case that just require three NFFTs of types 1 or 2 plus some additional fast Fourier transforms (FFTs). Fundamentally, they are based on exploiting the Lagrange formula structure. The proposed methods roughly provide a factor-ten improvement on the FM and CG alternatives in computational burden. The study includes several numerical examples in double precision, in which the proposed and the Gaussian elimination, CG and FM methods are compared, both in terms of round-off error and computational burden.2018-02-08T00:00:00Z