Methodologies for solving satellite remote sensing problems using neuro computing techniques:
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| Main Author: | |
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| Format: | Thesis Book |
| Language: | English |
| Published: |
München
Verl. Dr. Hut
2013
|
| Edition: | 1. Aufl. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXVIII, 207 S. graph. Darst. |
| ISBN: | 9783843910682 |
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| adam_text | TABLE OF CONTENTS
PART I: NEURO COMPUTING 1
1 COMPUTATIONAL INTELLIGENCE 3
1.1 INTRODUCTION 3
1.2 NEURO COMPUTING 4
1.3 GRANULAR COMPUTING 4
1.4 EVOLUTIONARY COMPUTING 5
1.5 BIOLOGY-INSPIRED COMPUTING 5
1.6 PROBABILISTIC COMPUTING 6
1.7 COMBINING COMPUTATIONAL INTELLIGENCE PARADIGMS 6
1.8 COMPUTATIONAL, ARTIFICIAL AND HUMAN INTELLIGENCE 7
1.9 SUMMARY 9
2 ARTIFICIAL NEURAL NETWORKS 11
2.1 INTRODUCTION 11
2.2 ARTIFICIAL NEURON 11
2.2.1 ACTIVATION FUNCTIONS 12
2.3 MULTI-LAYER PERCEPTRON 13
2.4 LEARNING PARADIGMS 14
2.4.1 BACKPROPAGATION LEARNING 15
2.4.2 OTHER LEARNING ALGORITHMS 16
2.5 RADIAL BASIS FUNCTION NETWORKS 16
2.6 KOHONEN S SELF-ORGANIZING NEURAL NETWORK 18
2.7 OTHER NEURAL NETWORKS 20
2.8 NNS AS UNIVERSAL FUNCTION APPROXIMATORS 20
2.9 TYPICAL NEURAL NETWORK APPLICATIONS 22
2.9.1 FUNCTION MAPPING 22
2.9.2 TIME SERIES PREDICTION 22
2.9.3 CLASSIFICATION 22
2.9.4 DATA MINING 22
2.10 SUMMARY 23
3 COMBINING NEURAL NETWORKS 25
IX
HTTP://D-NB.INFO/1036471462
R X ! TABLE OF CONTENTS
J J
3.1 INTRODUCTION 25
3.2 MODULAR COMBINATION OF NEURAL NETWORKS 26
3.2.1 MIXTURE-OF-EXPERTS 27
3.2.2 HIERARCHICAL MIXTURE-OF-EXPERTS 28
3.2.3 SIGMA-PI COMBINATION 29
3.3 ENSEMBLE COMBINATION OF NEURAL NETWORKS 29
3.3.1 BIAS AND VARIANCE DILEMMA 30
3.3.2 ENSEMBLE GENERATION TECHNIQUES 31
3.3.2.1 BAGGING 32
3.3.2.2 BOOSTING 32
3.3.2.3 RANDOM FORESTS 33
3.3.2.4 FEATUREBOOST 33
3.3.2.5 ARCMAE 34
3.3.2.6 STACKING 35
3.3.2.7 FEATING 36
3.3.3 ENSEMBLE MEMBERS SELECTION 36
3.3.3.1 INFORMATION THEORETIC SUBSET SELECTION 36
3.3.3.2 KAPPA PRUNING 38
3.3.3.3 GENETIC ALGORITHM SELECTION 39
3.3.4 ENSEMBLE COMBINATION OPERATORS 39
3.3.4.1 VOTING OPERATORS 39
3.3.4.2 SUM AND PRODUCT RULES 40
3.3.4.3 BAYESIAN AVERAGING 41
3.3.4.4 FUZZY AGGREGATION 42
3.3.4.5 OPTIMAL LINEAR COMBINATION 42
3.3.4.6 GENETIC AGGREGATION 43
3.3.5 ENSEMBLE LEARNING 44
3.3.5.1 DYNCO 44
3.3.5.2 NEGATIVE CORRELATION 45
3.3.5.3 LEAM-H- 46
3.3.5.4 BAIS 46
3.4 MODULAR VERSUS ENSEMBLE COMBINATION 46
3.5 DISADVANTAGES OF COMBINING NEURAL NETWORKS 47
3.6 SUMMARY 48
4 PROCESS MODEL FOR NEURO COMPUTING 49
4.1 INTRODUCTION 49
4.2 NEURO COMPUTING VERSUS TRADITIONAL COMPUTING 49
4.3 PROCESS MODELS IN SOFTWARE ENGINEERING 51
4.3.1 UNIFIED PROCESS: RUP 51
TABLE OF CONTENTS
4.3.2 AGILE MODELS: XP 53
4.4 LIFECYCLE FOR NEURO COMPUTING PROJECTS 53
4.4.1 REQUIREMENTS 54
4.4.2 ANALYSIS AND DESIGN 54
4.4.3 IMPLEMENTATION 54
4.4.4 TEST 54
4.4.5 DEPLOYMENT 55
4.4.6 PROJECT MANAGEMENT 55
4.4.7 CONFIGURATION AND CHANGE MANAGEMENT 55
4.4.8 ENVIRONMENT 55
4.4.9 APPLICABLE EXTREME PROGRAMMING PRACTICES 56
4.4.10 DOCUMENTATION 56
4.4.11 WORKFLOW INTERACTION 56
4.5 REQUIREMENTS IN NEURO COMPUTING 57
4.6 ANALYSIS AND DESIGN IN NEURO COMPUTING 58
4.7 IMPLEMENTATION IN NEURO COMPUTING 59
4.7.1 TRAINING DATASET GENERATION 59
4.7.1.1 DATA SAMPLING 60
4.7.1.2 DATA VALIDATION 60
4.7.2 TRAINING DATA CODING AND SCALING 61
4.7.3 TRAINING DATASETS SEPARATION 62
4.7.4 NETWORK DESIGN AND TRAINING 62
4.7.4.1 NETWORK TOPOLOGY SELECTION 63
4.7.4.2 AUTOMATIC NETWORK TOPOLOGY DETERMINATION 63
4.7.4.3 NETWORK TRAINING 64
4.7.5 NETWORK SELECTION AND COMBINATION 64
4.8 TESTING IN NEURO COMPUTING 65
4.8.1 NEURAL NETWORK VERIFICATION 65
4.8.1.1 GENERALIZATION 65
4.8.2 NEURAL NETWORK VALIDATION 66
4.8.2.1 MODEL ADEQUACY TESTING 66
4.8.2.2 RESIDUALS HISTOGRAM ANALYSIS 67
4.8.2.3 CONFIDENCE LIMITS DETERMINATION 68
4.8.2.4 QUALITY CONTROL 69
4.9 SUMMARY 70
PART II: REMOTE SENSING 71
5 REMOTE SENSING 73
XII
TABLE OF CONTENTS
5.1 INTRODUCTION 73
5.2 ELECTROMAGNETIC RADIATION 73
5.3 REMOTE SENSING TECHNIQUES 77
5.3.1 SENSOR TYPE 77
5.3.1.1 PASSIVE SENSORS 78
5.3.1.2 ACTIVE SENSORS 78
5.3.2 SENSOR PLATFORM 79
5.3.2.1 SATELLITE MEASUREMENTS 79
5.3.2.2 POLAR-ORBITING SATELLITES 80
5.3.2.3 GEOSTATIONARY SATELLITES 80
5.4 INFORMATION EXTRACTION IN REMOTE SENSING 80
5.5 TYPICAL REMOTE SENSING APPLICATIONS 82
5.6 NEURAL NETWORKS IN REMOTE SENSING 83
5.7 SUMMARY 84
6 ATMOSPHERIC REMOTE SENSING 85
6.1 INTRODUCTION 85
6.2 BACKSCATTERED UV/VIS RADIATION 86
6.3 FORWARD AND INVERSE PROBLEMS 87
6.4 RETRIEVAL ALGORITHMS 88
6.4.1 TOTAL COLUMN RETRIEVAL: DO AS 88
6.4.1.1 DOAS FITTING 88
6.4.1.2 AMF COMPUTATION 90
6.4.1.3 VERTICAL COLUMN DETERMINATION 91
6.4.1.4 VERTICAL COLUMN AS FIXED POINT PROBLEM 91
6.4.2 PROFILE RETRIEVAL: BAYESIAN METHOD 92
6.4.2.1 OPTIMAL ESTIMATE DETERMINATION 93
6.4.2.2 TRADITIONAL REGULARIZATION METHODS 94
6.4.2.3 REGULARIZATION WITH NEURAL NETWORKS 95
6.5 COMBINED TOTAL COLUMN AND PROFILE RETRIEVAL 95
6.6 SUMMARY 96
7 FORWARD MODELING USING NEURO COMPUTING 97
7.1 INTRODUCTION 97
7.2 MOTIVATION 97
7.3 FORWARD MODELING USING NEURO COMPUTING 98
7.3.1 DEVELOPMENT PROCESS 99
7.3.1.1 NODE POINTS GENERATION 99
7.3.1.2 MULTI-NETWORK DESIGN 100
7.4 CASE STUDY 1: AIR MASS FACTOR PARAMETERIZATION 100
TABLE OF CONTENTS !
XI I I
7.4.1 REQUIREMENTS 100
7.4.2 ANALYSIS AND DESIGN 101
7.4.3 IMPLEMENTATION 103
7.4.4 TEST 104
7.4.5 DEPLOYMENT 106
7.4.6 ACKNOWLEDGEMENTS 107
7.5 CASE STUDY 2: TOMSRAD PARAMETERIZATION 107
7.5.1 REQUIREMENTS 108
7.5.2 ANALYSIS AND DESIGN 108
7.5.3 IMPLEMENTATION 108
7.5.4 TEST 109
7.5.5 DEPLOYMENT 110
7.5.6 ACKNOWLEDGEMENTS 110
7.6 SUMMARY ILL
8 INVERSE MODELING USING NEURO COMPUTING 113
8.1 INTRODUCTION 113
8.2 MOTIVATION 113
8.2.1 NEURO COMPUTING INVERSION USING A STATISTICS-BASED APPROACH 114
8.2.2 NEURO COMPUTING INVERSION USING A PHYSICS-BASED APPROACH 114
8.3 INVERSE MODELING USING NEURO COMPUTING 115
8.3.1 DIMENSIONALITY REDUCTION 116
8.3.2 LEARNING AN INVERSE FUNCTION 117
8.3.2.1 EFFECTS OF THE MEASUREMENT AND RETRIEVAL NOISE 118
8.3.3 SOLVING ILL-POSED PROBLEMS 119
8.3.3.1 SELECTIVE PRUNNING AND SPREAD ENCODING 119
8.3.3.2 REGULARIZATION 120
8.3.3.3 MODEL COMBINATION 120
8.3.4 GLOBAL VS. LOCAL MINIMIZATION 121
8.3.5 DEVELOPMENT PROCESS 121
8.3.5.1 NODE POINTS GENERATION 121
8.3.6 MODEL INTERPRETATION 122
8.4 CASE STUDY: RETRIEVAL OF CLOUD PARAMETERS 124
8.4.1 INTRODUCTION 124
8.4.2 REQUIREMENTS 125
8.4.3 ANALYSIS AND DESIGN 125
8.4.4 IMPLEMENTATION 126
8.4.5 VERIFICATION AND VALIDATION 131
8.4.6 DEPLOYMENT 132
8.4.7 ACKNOWLEDGEMENTS 133
| XIV J TABLE
OF CONTENTS
8.5 SUMMARY 133
PART III: APPLICATIONS 135
9 OZONE MONITORING 137
9.1 INTRODUCTION 137
9.2 GOME INSTRUMENT 139
9.3 GOME PROCESSING SYSTEM 139
9.3.1 GOME NEAR-REAL-TIME PROCESSING 142
9.4 GLOBAL OZONE MONITORING 142
9.4.1 GLOBAL CHANGES 143
9.5 OZONE DEPLETION 147
9.5.1 ANTARCTIC OZONE HOLE 147
9.5.2 ARCTIC OZONE MINI-HOLES 151
9.5.3 ACKNOWLEDGEMENTS 151
9.6 SUMMARY 152
10 CLOUD VARIABILITY 153
10.1 INTRODUCTION 153
10.2 CLOUD FRACTION DETERMINATION 154
10.2.1 MOTIVATION 154
10.2.2 CLOUD RECOGNITION ALGORITHM 155
10.2.2.1 PRE-PROCESSING BASED ON AN INVARIANT IMAGE FORMATION MODEL 156
10.2.2.2 FUSION BY PIXEL SELECTION USING RGB COLORS 158
10.2.2.3 CLOUD-FREE COMPOSITE GENERATION 159
10.2.2.4 CLOUD RECOGNITION WITH OCRA 160
10.2.3 APPLICATION TO GOME DATA 160
10.2.3.1 REQUIREMENTS 160
10.2.3.2 ANALYSIS AND DESIGN 160
10.2.3.3 IMPLEMENTATION 161
10.2.3.4 VERIFICATION AND VALIDATION 163
10.3 CLOUD-TOP HEIGHT AND ALBEDO DETERMINATION 164
10.4 CLOUD-TOP PRESSURE AND CLOUD OPTICAL THICKNESS 164
10.5 GLOBAL CLOUD VARIABILITY AS MEASURED BY GOME 166
10.5.1 COMPUTATION OF MONTHLY MEAN CLOUD AVERAGES 166
10.5.2 CLOUD PROPERTIES DISTRIBUTION 167
10.5.3 ANOMALIES, DEVIATIONS AND TRENDS OF CLOUD PROPERTIES 173
10.5.4 CLOUD FREQUENCY DISTRIBUTIONS 175
10.5.5 CLOUD TYPE CLASSIFICATION 175
TABLE OF CONTENTS
J
XV
J
10.5.6 CORRELATED VARIATIONS OF CLOUD PROPERTIES 178
10.5.7 ACKNOWLEDGEMENTS 178
10.6 SUMMARY 178
APPENDICES 179
A ABBREVIATIONS AND ACRONYMS 181
BIBLIOGRAPHY 185
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| spelling | Loyola Rodriguez, Diego G. Verfasser aut Methodologies for solving satellite remote sensing problems using neuro computing techniques Diego G. Loyola Rodriguez 1. Aufl. München Verl. Dr. Hut 2013 XXVIII, 207 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Zugl.: München, Techn. Univ., Diss., 2013 Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Neurocomputer (DE-588)4200446-9 gnd rswk-swf Satellitenfernerkundung (DE-588)4224344-0 gnd rswk-swf Erderkundungssatellit (DE-588)4271759-0 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Satellitenfernerkundung (DE-588)4224344-0 s Erderkundungssatellit (DE-588)4271759-0 s Neurocomputer (DE-588)4200446-9 s Neuronales Netz (DE-588)4226127-2 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026202459&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Loyola Rodriguez, Diego G. Methodologies for solving satellite remote sensing problems using neuro computing techniques Neuronales Netz (DE-588)4226127-2 gnd Neurocomputer (DE-588)4200446-9 gnd Satellitenfernerkundung (DE-588)4224344-0 gnd Erderkundungssatellit (DE-588)4271759-0 gnd |
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| title | Methodologies for solving satellite remote sensing problems using neuro computing techniques |
| title_auth | Methodologies for solving satellite remote sensing problems using neuro computing techniques |
| title_exact_search | Methodologies for solving satellite remote sensing problems using neuro computing techniques |
| title_full | Methodologies for solving satellite remote sensing problems using neuro computing techniques Diego G. Loyola Rodriguez |
| title_fullStr | Methodologies for solving satellite remote sensing problems using neuro computing techniques Diego G. Loyola Rodriguez |
| title_full_unstemmed | Methodologies for solving satellite remote sensing problems using neuro computing techniques Diego G. Loyola Rodriguez |
| title_short | Methodologies for solving satellite remote sensing problems using neuro computing techniques |
| title_sort | methodologies for solving satellite remote sensing problems using neuro computing techniques |
| topic | Neuronales Netz (DE-588)4226127-2 gnd Neurocomputer (DE-588)4200446-9 gnd Satellitenfernerkundung (DE-588)4224344-0 gnd Erderkundungssatellit (DE-588)4271759-0 gnd |
| topic_facet | Neuronales Netz Neurocomputer Satellitenfernerkundung Erderkundungssatellit Hochschulschrift |
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