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Castelletti-Thesis.Pdf PhD Dissertation International Doctorate School in Information and Communication Technologies DISI - University of Trento Advanced regression and detection methods for remote sensing data analysis Davide Castelletti Advisor: Lorenzo Bruzzone Co-advisor: Beg¨umDemir April 2017 Abstract Nowadays the analysis of remote sensing data for environmental monitoring is funda- mental to understand the local and global Earth dynamics. In this context, the main goal of this thesis is to present novel signal processing methods for the estimation of biophysical parameters and for the analysis icy terrain with active sensors. The thesis presents three main contributions. In the context of biophysical parameters estimation we focus on regression methods. According to the analysis of the literature, most of the regression techniques require a rel- evant number of reference samples to model a robust regression function. However, in real-word applications the ground truth observations are limited as their collection leads to high operational cost. Moreover, the availability of biased samples may result in low estimation accuracy. To address these issues, in this thesis we propose two novel contribu- tions. The first contribution is a method for the estimation of biophysical parameters that integrates theoretical models with empirical observations associated to a small number of in-situ reference samples. The proposed method computes and correct deviations between estimates obtained through the inversion of theoretical models and empirical observations. The second contribution is a semisupervised learning (SSL) method for regression defined in the context of the "-insensitive SVR. The proposed SSL method aims to mitigate the problems of small-sized biased training sets by injecting priors information in the initial learning of the SVR function, and jointly exploiting labeled and unlabeled samples in the learning phase of the SVR. The third contribution of this dissertation addresses the clutter detection problem in radar sounder (RS) data. The capability to detect clutter is fundamental for the inter- pretation of subsurface features in the radargram. In the state of the art, techniques that require accurate information on the surface topography or approaches that exploit com- plex multi-channel radar sounder systems have been presented. In this thesis, we propose a novel method for clutter detection that is independent from ancillary information and limits the hardware complexity of the radar system. The method relies on the interferomet- ric analysis of two-channel RS data and discriminates the clutter and subsurface echoes by modeling the theoretical phase difference between the cross-track antennas of the RS. This allows the comparison of the phase difference distributions of real and simulated data. Qualitative and quantitative experimental results obtained on real airborne SAR and RS data confirm the effectiveness of the proposed methods. Keywords[Remote sensing, signal processing, biophysical parameters estimation, sup- port vector regression, synthetic aperture radar, radar sounder, clutter detection.] i ii Contents 1 Introduction 1 1.1 Background and motivation . .1 1.2 Objectives and novel contributions of the thesis . .4 1.3 Biophysical parameter estimation . .5 1.4 Clutter detection in radar sounder signals . .6 1.5 Structure of the Thesis . .7 2 Fundamentals and background 9 2.1 Introduction to microwave remote sensing systems . .9 2.1.1 Radar . .9 2.1.2 Synthetic aperture radar . 11 2.1.3 Radar interferometry . 13 2.1.4 Radar sounder . 16 2.2 Overview on approaches for biophysical parameters estimation . 19 2.2.1 Biophysical parameters estimation problem . 20 2.2.2 Derivation of empirical data-driven relationships using empirical methods . 20 2.2.3 Inversion of physical based theoretical models . 21 2.3 Overview on clutter in radar sounder data . 22 2.3.1 Clutter problem definition . 23 2.3.2 Clutter suppression techniques . 24 2.3.3 Detection techniques . 25 3 A novel hybrid method for the correction of the theoretical model in- version in bio/geophysical parameter estimation 27 3.1 Introduction . 27 3.2 Proposed hybrid method . 30 3.2.1 Computation and modeling of deviations . 31 3.2.2 Estimation of the final target values . 33 iii 3.3 Datasets description and experimental setup . 34 3.4 Experimental results . 36 3.4.1 Results on the Scatt dataset . 36 3.4.2 Results on the SMEX dataset . 39 3.4.3 Results on the ActPass dataset . 41 3.5 Conclusion . 43 4 A novel semisupervised method for the estimation of biophysical pa- rameters based on Support Vector Regression 45 4.1 Introduction . 46 4.2 Problem definition . 49 4.3 Proposed SSL method for regression . 50 4.3.1 Injecting priors information in the initial learning of the SVR function 51 4.3.2 Exploitation of both labeled and unlabeled samples in the learning process of the regression function . 54 4.4 Dataset description and design of the experiments . 58 4.5 Experimental results . 60 4.5.1 Paneveggio dataset: estimation of stem diameter . 60 4.5.2 Abalone dataset: age of abalone . 62 4.6 Conclusion . 64 5 A novel technique based on interferometry for clutter detection in two- channel radar sounder data 67 5.1 Introduction . 67 5.2 Proposed technique for cross track clutter detection . 70 5.2.1 Manual feature extraction and theoretical phase difference estimation 72 5.2.2 Interferogram formation for cross-channel phase difference . 74 5.2.3 Comparison of theoretical and real phase difference distributions . 76 5.3 Dataset description and experimental results . 77 5.3.1 MARFA radar system description . 78 5.3.2 Dataset 1: deep subsurface . 80 5.3.3 Dataset 2: Clutter feature detection . 83 5.3.4 Dataset 3: Clutter and subsurface discrimination . 85 5.4 Cross track clutter detection in planetary RS . 90 5.5 Discussion and conclusion . 94 6 Conclusions and future work 95 6.1 Research context and summary of novel contributions . 95 iv 6.2 Concluding remarks and future developments . 98 7 List of Publications 101 7.1 Journal Papers . 101 7.2 Conferences . 101 Bibliography 103 v List of Tables 3.1 Main characteristics of the three datasets considered in the experimental analysis. 35 3.2 Estimation performances obtained by the proposed hybrid approach with the GDB and LDB strategies and the standard inverse theoretical model (Scatt dataset). 37 3.3 Estimation performances obtained by the proposed hybrid approach with the GDB and LDB strategies and the standard inverse theoretical model (SMEX dataset). 39 3.4 Estimation performances obtained by the proposed hybrid approach with the GDB and LDB strategies and the standard inverse theoretical model (ActPass dataset). 41 4.1 The list of variables used in the experiments and their physical meanings for the estimation of stem diameter. 58 4.2 The list of variables used in the experiments and their physical meanings for the estimation of abalones ages. 59 4.3 Mean and standard deviation (std) of MAE and R2 obtained by the stan- dard SVR and proposed SS-SVR. 61 4.4 Results of the first step of proposed method obtained when the value of radius is estimated either by the proposed strategy or by randomly fixing its value. 61 4.5 Mean and standard deviation (std) of MAE and R2 obtained by executing the proposed SS-SVR with and without the 2nd step. 62 4.6 Mean and standard deviation (std) of MAE and R2 obtained by the stan- dard SVR and proposed SS-SVR. 63 4.7 Comparison of the results of the first step of proposed method obtained when the value of radius is estimated by either the proposed strategy or by randomly fixing its value. 63 vii 4.8 Mean and standard deviation (std) of MAE and R2 obtained by executing the proposed SS-SVR with and without the 2nd step. 63 5.1 Typical operating parameters for MARFA RS. 79 5.2 SAD obtained between the real and the simulated (on both nadir and clutter hypothesis) histograms and mean of the simulated distributions. 82 5.3 SAD obtained on real and simulated (on both nadir and clutter hypothesis) histograms and mean of the simulated distributions for the selected feature. 85 5.4 SAD values obtained between the real and the simulated (on both nadir and clutter hypothesis) histograms and the mean of the simulated distributions for feature f1................................... 89 5.5 SAD values obtained between the real and the simulated (on both nadir and clutter hypothesis) histograms and mean of the simulated distributions for feature f2................................... 89 viii List of Figures 2.1 Monostatic radar configuration. 10 2.2 Principle of SAR. 11 2.3 SAR acquisition geometry. 12 2.4 Interferometric radar configurations. 15 2.5 Exact formula of the interferometric phase in the cross-track single-pass configuration. 16 2.6 Nadir acquisition geometry of radar sounder systems. 17 2.7 Geometry of the clutter problem. 23 2.8 Surface clutter contribution before SAR processing (light gray locus), and after focusing in azimuth direction by Doppler processing and in range by range compression (dark gray cells). This example assumes flat topography in the absence of airplane roll. 24 3.1 Block diagram of the proposed hybrid approach. 31 3.2 Scatterplots of measured versus estimated dielectric constant values ob- tained by (a) the inverse theoretical model, (b) the proposed hybrid ap- proach with the GDB strategy, and (c) the proposed hybrid approach with the LDB strategy (Scatt dataset with HH23 features). 38 3.3 Scatterplots of measured versus estimated dielectric constant values ob- tained by (a) the inverse theoretical model, (b) the proposed hybrid ap- proach with the GDB strategy, and (c) the proposed hybrid approach with the LDB strategy (SMEX dataset). 40 3.4 Scatterplots of measured versus estimated dielectric constant values ob- tained by (a) the inverse theoretical model, (b) the proposed hybrid ap- proach with the GDB strategy, and (c) the proposed hybrid approach with the LDB strategy (ActPass dataset with the HHVV feature).
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