Infrared Thermographic NDT-based Damage Detection and Analysis Method for Spacecraft

Preface
	Overview of the Book
Contents
Acronyms
1 Background and Requirements
	1.1 Space Environment Effects on Spacecraft
		1.1.1 Vacuum Environment
		1.1.2 Space Debris Environment
		1.1.3 Solar Irradiation Environment
		1.1.4 Atomic Oxygen Environment
	1.2 Spacecraft Materials and Damage
		1.2.1 Introduction to Spacecraft Materials
		1.2.2 Typical Spacecraft Material Damage
	1.3 Overview of Infrared Thermographic NDT Technology
		1.3.1 Basic Concepts of Infrared Thermographic NDT Technology
		1.3.2 Basic Principles of Infrared Thermographic NDT Technology
		1.3.3 Infrared Thermographic NDT System Composition
	References
2 Infrared Feature Extraction and Damage Reconstruction
	2.1 Introduction
	2.2 Variable-Step-Based Pre-processing of Infrared Thermographic Image Sequence
		2.2.1 Data Block Division Based on Thermal Extremes
		2.2.2 Various Regional Step Sizes Establishment
		2.2.3 Variable Step Redundant Information Removal
	2.3 Transient Thermal Responses Separation Based on Clustering
		2.3.1 GMM-Based Clustering of Transient Thermal Responses
		2.3.2 DBSCAN-Based Clustering of Transient Thermal Responses
	2.4 Representative Transient Thermal Response Extraction and Image Reconstruction
		2.4.1 Representative TTR Extraction Based on Local Average Performance
		2.4.2 Representative TTR Extraction Based on Distances
		2.4.3 Representative TTR Extraction Based on Distance Weighting
	2.5 Experimental Results and Analysis
	2.6 Summary
	References
3 Reconstructed Thermal Image Fusion Based on Multi-objective Guided Filtering
	3.1 Introduction
	3.2 Complex Damage Fusion Requirement
	3.3 Multiple Fusion Objectives Jointly Moulding
		3.3.1 Thermal Radiation Variance-Aware Objective Function
		3.3.2 Multi-window Edge-Aware Objective Function
		3.3.3 Local Detail Extraction Objective Function
	3.4 Multi-objective Guided Filtering Based Weight Acquisition Layer
		3.4.1 Two-Layer Multi-objective Fusion Framework
		3.4.2 Multi-objective Decomposition Based on Penalty Term
		3.4.3 Implementation of Multi-objective Guided Filtering Based Weight Acquisition Layer
	3.5 Multi-scale Fusion of Full Pixel Layers Based on Optimal Weight
		3.5.1 Dual Scale Decomposition of Reconstructed Thermal Images on Full Pixel Layers
		3.5.2 Multi-guided Filtering Based Weight Map Acquisition
		3.5.3 All-Pixel Image Fusion Implementation with Multi-objective Guided Filtering
	3.6 Experimental Results and Analysis
		3.6.1 Specimen #1
		3.6.2 Specimen #2
	3.7 Summary
	References
4 Stitching Technique for Reconstructed Thermal Images
	4.1 Introduction
	4.2 Feature Extraction Techniques for Reconstructed Thermal Images
		4.2.1 Feature Points of Reconstructed Thermal Images
		4.2.2 FAST Feature Extraction of Reconstructed Thermal Images
		4.2.3 Fine Feature Extraction of Reconstructed Thermal Images
	4.3 Alignment Techniques for Reconstructed Thermal Image's Feature Points
		4.3.1 Alignment of Feature Points of Reconstructed Thermal Images
		4.3.2 Analysis of Reconstructed Thermal Image's Feature Point Alignment Techniques
	4.4 Stitching Quality Improvement Method of Reconstructed Thermal Images
		4.4.1 Seamless Stitching Fusion of Stitched Reconstructed Thermal Images
		4.4.2 Natural Stitching Method with Large Parallax for Reconstructed Thermal Images
	4.5 Experiment and Analysis
		4.5.1 Fast Feature Extraction Stitching Experiment for Reconstructed Thermal Images
		4.5.2 Fine Feature Extraction Stitching Experiments of Reconstructed Thermal Images
	4.6 Summary
	References
5 Weight Vector Adjustment-Based Multi-objective Segmentation of Reconstructed Thermal Images
	5.1 Introduction
	5.2 The Challenge of Complex Damage Segmentation
	5.3 Complex Object-Oriented Infrared Image Segmentation Objectives
		5.3.1 Noise-Cancellation Oriented Segmentation Objective for Complex Damage Reconstructed Thermal Images
		5.3.2 Detail-Preserving Oriented Segmentation Objective for Complex Damage Reconstructed Thermal Images
		5.3.3 Edge-Retention Oriented Segmentation Objective for Complex Damage Reconstructed Thermal Images
	5.4 Multi-objective Model Construction and Irregular Pareto Front Analysis
		5.4.1 Multi-objective Modelling and Complex Damage Segmentation Framework
		5.4.2 Irregular Pareto Front and Necessity of Adjustment
	5.5 Crowding Degree Adaptive and Chebyshev Decomposition Based …
		5.5.1 Chebyshev Decomposition
		5.5.2 Crowding Metric Based on Manhattan Distance
		5.5.3 Weight Vector Adjustment
		5.5.4 Implementation of the Weight Vector Adaptive Adjustment Method
	5.6 Effective Area Incremental Learning and PDM Based Weight …
		5.6.1 Effective Area and Active Vectors
		5.6.2 PDM and Population Evolution
		5.6.3 Cascade Clustering Based Dominant Solution Selection
		5.6.4 Incremental SVM-Based Weight Vector Learning and Tuning
	5.7 Experimental Results and Analysis
		5.7.1 Crowding Degree Based Adaptive Weight Vector Adjustment
		5.7.2 Effective Area and PDM Based Adaptive Weight Vector Adjustment
	5.8 Summary
	References
6 Defects Positioning Method for Large Size Specimen
	6.1 Introduction
	6.2 Defect Positioning Based on Whole and Local View Conversion …
		6.2.1 Global Defect Location Labeling for Stitched Reconstructed Thermal Images
		6.2.2 Precise Positioning of Defective Regions
		6.2.3 Re-inspection of Defective Regions After Precise Positioning
	6.3 Defect Positioning Based on Inverse Heterogeneous Source …
		6.3.1 Pixel Conversion of Stitched Reconstructed Thermal Images
		6.3.2 Determining Method of Image Overlap Area
		6.3.3 Defect Contour Positioning for Regional Determination Results
	6.4 Experiment and Analysis
		6.4.1 Experiment and Analysis of Defect Positioning Based on Whole-Local Perspective
		6.4.2 Experiment and Analysis of Defect Positioning Based on Inverse Heterogeneous Sources
	6.5 Summary
	References
7 Defect Edge Detection and Quantitative Calculation of Reconstructed Thermal Images
	7.1 Introduction
	7.2 Pixel-Level Edge Detection of Defective Regions …
		7.2.1 Pixel-Level Edge Detection Based on Differential Operators
		7.2.2 Pixel-Level Edge Detection Based on Canny Composite Operator
	7.3 Sub-Pixel Level Edge Detection of Defective Regions in Reconstructed …
		7.3.1 Sub-pixel Fitting Method Based on Edge Pixel Position
		7.3.2 Sub-pixel Detection Method Based on Image Zernike Moments
	7.4 Quantitative Calculation of Defective Regions  in Reconstructed Thermal Images
		7.4.1 Calculation of Geometric Feature Parameters
		7.4.2 Calculation of Morphological Distribution Parameters
	7.5 Experiment and Analysis
		7.5.1 Edge Detection Experiments for Specimen Hyper-1
		7.5.2 Sub-pixel Edge Detection Experiment for Specimen Hyper-1
		7.5.3 Quantitative Calculation of Defective Regions for Specimen #1
		7.5.4 Quantitative Calculation of Defective Regions of Specimen #2
		7.5.5 Defective Regions Positioning Experiments of Specimen Hyper-1
	7.6 Summary
	References