| Description |
1 online resource (380 p.) |
| Series |
SAR Remote Sensing Ser |
|
SAR Remote Sensing Ser
|
| Contents |
Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgements -- The Authors -- List of Abbreviations -- List of Figures and Tables -- Part I: Sarcheology: The Era of the Big Radar Mosaics -- Chapter 1: The Dawn of the SAR Mosaics Era: The ESA-JRC Central Africa Mosaic Project -- 1.1 Radar Mosaics: What and Why -- 1.2 The CAMP Data Processing Machine -- 1.3 Radiometry -- 1.3.1 Radiometric Changes in Time -- 1.3.2 Within Tile Radiometric Changes in range -- 1.3.3 Quantization Noise -- Note -- References -- Chapter 2: The L-Band Breed: The GRFM Africa Radar Mosaic -- 2.1 The GRFM project -- 2.2 The GRFM Africa Processing Chain -- 2.2.1 Input Datasets -- 2.2.2 Data Flow -- 2.3 Geolocation -- 2.3.1 The Block Adjustment Method -- 2.3.2 Geolocation Validation -- 2.4 Wavelet Multiresolution Decomposition -- 2.4.1 Multiresolution Products -- 2.5 The GRFM Africa Mosaic Second Edition -- References -- Chapter 3: The GRFM-CAMP Thematic Products -- 3.1 From Backscatter to a Thematic Map -- 3.2 Vegetation Classes -- 3.3 Map Compilation Methods -- 3.4 Complementarity of Radar Sensors -- 3.5 Validation -- 3.6 Tour of Relevant Features -- References -- Chapter 4: Evolution of the Species: The ALOS PALSAR Africa Mosaic -- 4.1 Introduction -- 4.2 The Mosaic Processing Chain -- 4.3 Correction of Range Dependent Radiometric Bias in Path Images -- 4.4 Correction for Additive Thermal Noise in HV Strip Images -- 4.5 Radiometric Inter-strip Mosaic Balancing -- 4.6 Geocoding -- 4.7 Radiometric Normalization for Topographic Effects -- 4.7.1 Correction of Effective Scattering Area -- 4.7.2 Correction for the Dependence of the Backscattering Coefficient on Incidence Angle -- 4.7.3 Assessment of the Radiometric Correction for Topography -- 4.8 Overview of the Thematic Information Content |
|
4.8.1 Comparison with the GRFM Africa Dataset -- 4.8.2 Grass and Woody Savannas -- 4.8.3 Flooded Forest -- 4.8.4 Plantations -- 4.8.5 Secondary Forest -- References -- Part II: Measures of SAR Random Fields in the Scale-Space-Time Domain -- Chapter 5: The Stuff Backscatter Random Fields Are Made Of -- 5.1 Introduction -- 5.2 Transport Theory -- 5.2.1 An Illustrative Case: Propagation Through A Plane Parallel Medium -- 5.3 The UTA Wave Scattering Model for Layered Vegetation -- 5.4 Backscatter Simulation for a Dense Tropical Primary Rain Forest -- References -- Chapter 6: Statistical Measures of SAR Random Spatial Fields: Fingerprints of the Forest Structure -- 6.1 Introduction -- 6.2 Random Fields from Backscatter Observations -- 6.3 Random Fields from InSAR Coherence Observations -- 6.4 Wavelet Based Textural Measures of Random Fields -- 6.5 Connection between Wavelet Space-Scale Analysis and Fourier Spectral Analysis -- 6.5.1 White Noise -- 6.5.2 1/f Process -- 6.5.3 Correlated Surface (Gamma Distributed RCS) with Exponential ACF (Lorentzian Spectrum) -- 6.5.4 Correlated Surface with Exponential Cosine ACF -- 6.5.5 Effects from Coherent Imaging and Illumination Beam Size -- 6.5.6 Cross-correlation between Two Stationary Processes with a Gaussian CCF -- 6.6 Accuracy of Wavelet Variance Estimators -- 6.6.1 Prelude: Probability Density Function of the Wavelet Coefficients of a Speckle Pattern -- 6.6.2 Expected Value and Variance of the Wavelet Variance Estimator -- 6.6.2.1 Uncorrelated Speckle Pattern -- 6.6.2.2 Correlated Speckle -- 6.7 Tools for Textural Analysis of SAR Random Fields -- 6.7.1 A Multi-Voice Discrete Wavelet Transform -- 6.7.2 Wavelet Signatures -- 6.7.3 Wavelet Spectra -- 6.8 WASS Analysis of SAR Backscatter Fields -- 6.8.1 Lowland Rainforest and Swamp Forest Signatures in ERS-1 Data |
|
6.8.2 TanDEM-X Signatures in the same Thematic Context -- 6.8.3 Intact and Degraded Forest Detection by Functional Analysis of WASS Signatures -- 6.9 WASS Analysis of InSAR and LiDAR Digital Surface Models -- 6.10 2D Wavelet Variance Spectra of Backscatter Fields: Toward a Textural Classifier -- 6.10.1 A Test Case: Texture-Based Forest Mapping in the Congo Floodplain by ERS-1 Data -- 6.10.2 Floodplain Mapping Revisited by Sentinel-1 data -- 6.10.3 An (Experimental) Wavelet Spectrum Functional Classifier -- 6.11 Extension to Polarimetry -- 6.11.1 The WASP of Correlated Backscatter Textures: A Numerical Model -- 6.11.2 WASP Analysis of a PALSAR Full-Pol Data Set -- Note -- References -- Chapter 7: Hitting Corners: The Lipschitz Regularity, a Measure of Discontinuities in Radar Images Connected with Forest Spatial Distribution -- 7.1 Introduction -- 7.2 The Lipschitz Condition -- 7.3 Singular Functions and Lip Parameters Estimated by Wavelet Maxima Trajectories in the Scale Domain -- 7.3.1 Step Function -- 7.3.2 Cusp -- 7.3.3 Impulse -- 7.3.4 Smoothed Singularity -- 7.3.5 Non-Isolated Singularities -- 7.3.6 Effect of Speckle -- 7.4 A Monte Carlo Simulator of Polarimetric SAR Backscatter Discontinuities -- 7.5 Experiments Using Simulated Signals -- 7.5.1 Toy Signals with Simple Discontinuities -- 7.5.2 Margin between a Clear-Cut and a Dense Forest -- 7.5.3 Edge on Tilted Terrain -- 7.6 Lipschitz Regularity in Real SAR Data -- 7.6.1 TanDEM-X Backscatter Data -- 7.6.2 TanDEM-X Coherence Data -- 7.7 Image-Wide Representations of Lipschitz Parameters -- References -- Chapter 8: The Beauty Farm: A Wavelet Method for Edge Preserving Piece-wise Smooth Approximations of Radar Images -- 8.1 The Image Model and a Conceptual View of the Method -- 8.2 The Computational Engine -- 8.3 Problems Related to Multiplicative Speckle Noise |
|
8.4 Issues Related to Textural Edges -- 8.5 Maxima Linking -- 8.6 From Theory to Practice: A Tropical Forest Cover Mapping Exercise Using Smooth Approximations of GRFM SAR Data -- 8.6.1 Processing Methods -- 8.6.1.1 Region Growing -- 8.6.1.2 NMP Classifier -- 8.6.2 Test Sites and Thematic Class Definition -- 8.6.3 Selected Results -- References -- Chapter 9: The Cleaning Service: A Multi-temporal InSAR Coherence Magnitude Filter -- 9.1 Rationale -- 9.2 The Filter Machinery -- 9.3 Generation of a Testing Dataset -- 9.4 Test Cases Using TanDEM-X Data -- 9.5 Temporal Features -- References -- Chapter 10: Proxies of Forest Volume Loss and Gain by Differencing InSAR DSMs: Fingerprints of Forest Disturbance -- 10.1 Motivation -- 10.2 Study Site -- 10.3 TanDEM-X Data -- 10.4 Methods -- 10.4.1 DSM Difference Data Set Generation and Calibration -- 10.4.2 Object-Based Change Detection -- 10.4.3 Change Objects Refinement -- 10.4.4 Variance of the Within-Object Mean Height Difference Estimator -- 10.4.5 Effect Size -- 10.4.6 Probability of object detection by statistical decision theory -- 10.4.6.1 Neyman-Pearson approach -- 10.4.6.2 Bayesian Approach -- 10.4.7 Object Shape -- 10.4.8 Characterization of Objects by Contextual Information -- 10.4.8.1 Distance from Roads -- 10.4.8.2 Attributes by Land Management -- 10.5 Factors Influencing the DSM Change Magnitude -- 10.5.1 Forest Vertical Structure and Spatial Distribution (Forest Density) -- 10.5.2 Environmental Conditions (Seasonality and Rainfall) -- 10.5.3 Dependence on Instrument Parameters -- 10.5.3.1 Volume Only -- 10.5.3.2 Volume over Ground -- 10.6 Analysis -- 10.6.1 ∆ DSM Magnitude and Area Descriptive Statistic -- 10.6.2 Standard Error of the Object Mean -- 10.6.3 Effect Size -- 10.6.4 Object Detection by Statistical Decision Theory -- 10.6.5 Spatial Location of Objects |
| Notes |
Description based upon print version of record |
| Subject |
Rain forests -- Remote sensing
|
|
Forest surveys -- Data processing
|
|
Synthetic aperture radar.
|
|
Spatial analysis (Statistics)
|
|
spatial analysis.
|
|
Forest surveys -- Data processing
|
|
Rain forests -- Remote sensing
|
|
Spatial analysis (Statistics)
|
|
Synthetic aperture radar
|
| Form |
Electronic book
|
| Author |
De Grandi, Elsa Carla
|
| ISBN |
9781000364781 |
|
100036478X |
|
