目錄
“地球觀測與導(dǎo)航技術(shù)叢書”編寫說明
序一
序二
序三
序四
第三版前言
第二版前言
第一版前言
第1章 緒論 1
1.1 空間數(shù)據(jù)挖掘的由來 1
1.2 空間數(shù)據(jù)挖掘的價(jià)值 8
1.3 空間數(shù)據(jù)挖掘的難題 10
1.4 本書的內(nèi)容和組織結(jié)構(gòu) 13
第2章 空間數(shù)據(jù)挖掘基礎(chǔ) 16
2.1 基本定義和性質(zhì) 16
2.2 空間數(shù)據(jù)挖掘金字塔 19
2.3 空間數(shù)據(jù)挖掘視角 23
2.4 空間數(shù)據(jù)挖掘的知識(shí)類型 30
2.5 空間知識(shí)的表達(dá) 33
2.6 空間數(shù)據(jù)挖掘與相關(guān)學(xué)科的關(guān)系 35
第3章 空間數(shù)據(jù)挖掘的數(shù)據(jù)源 39
3.1 空間數(shù)據(jù)的內(nèi)容和特性 39
3.2 空間數(shù)據(jù)獲取 42
3.3 空間數(shù)據(jù)結(jié)構(gòu) 44
3.4 空間數(shù)據(jù)模型 47
3.5 空間數(shù)據(jù)的組織和管理 52
3.6 國家空間數(shù)據(jù)基礎(chǔ)設(shè)施 57
3.7 中國國家空間數(shù)據(jù)基礎(chǔ)設(shè)施 62
3.8 從空間數(shù)據(jù)基礎(chǔ)設(shè)施到大數(shù)據(jù) 66
第4章 空間數(shù)據(jù)清理 71
4.1 空間數(shù)據(jù)的污染問題 71
4.2 空間數(shù)據(jù)清理的基本內(nèi)容 76
4.3 空間觀測數(shù)據(jù)的清理 78
4.4 遙感圖像的清理 94
4.5 基于DHP法的空間數(shù)據(jù)選擇 107
第5章 空間數(shù)據(jù)挖掘可用的理論方法 112
5.1 確定集合論 112
5.2 擴(kuò)展集合論 118
5.3 仿生學(xué)方法 122
5.4 知識(shí)圖譜 127
5.5 可視化 127
5.6 空間數(shù)據(jù)挖掘系統(tǒng) 128
第6章 數(shù)據(jù)場 132
6.1 數(shù)據(jù)輻射 132
6.2 數(shù)據(jù)場的概念和性質(zhì) 135
6.3 數(shù)據(jù)場的場強(qiáng)和勢 139
第7章 云模型 147
7.1 隨機(jī)性和模糊性 147
7.2 云模型的概念 148
7.3 云模型的類型 151
7.4 云發(fā)生器 155
7.5 云變換 161
7.6 基于云模型的不確定推理 163
第8章 地學(xué)粗空間和概念格 166
8.1 地學(xué)粗空間的內(nèi)涵 166
8.2 地學(xué)粗空間在地球空間信息學(xué)的外延 176
8.3 概念格及其性質(zhì) 178
8.4 概念格的構(gòu)建 185
第9章 遙感圖像智能檢索 196
9.1 圖像檢索特征 196
9.2 遙感圖像統(tǒng)計(jì)檢索 199
9.3 遙感圖像深度檢索 206
第10章 遙感圖像分類 212
10.1 基于歸納學(xué)習(xí)和貝葉斯方法的圖像分類 212
10.2 基于云模型的圖像分類 216
10.3 基于粗神經(jīng)網(wǎng)絡(luò)的圖像分類 218
10.4 基于地學(xué)粗空間的專題提取 219
第11章 遙感圖像變化檢測 221
11.1 變化檢測的方法體系 221
11.2 面向?qū)ο髾C(jī)器學(xué)習(xí)的遙感圖像變化檢測 224
11.3 顧及空間特征的時(shí)間序列變化檢測 230
11.4 面向無人機(jī)視頻的動(dòng)態(tài)目標(biāo)跟蹤 237
第12章 時(shí)空分布的視頻數(shù)據(jù)挖掘 244
12.1 視頻數(shù)據(jù)智能壓縮與云存儲(chǔ) 244
12.2 基于內(nèi)容的視頻數(shù)據(jù)檢索 245
12.3 時(shí)空視頻數(shù)據(jù)挖掘 247
12.4 視頻人臉超分辨率識(shí)別與表情挖掘 249
12.5 基于長程背景字典的衛(wèi)星視頻編碼 262
第13章 夜光遙感圖像挖掘 267
13.1 夜光遙感圖像挖掘的必要性 267
13.2 中國區(qū)域經(jīng)濟(jì)分析 268
13.3 “一帶一路”城市發(fā)展評(píng)估 271
13.4 人道主義災(zāi)難評(píng)估 274
第14章 寶塔滑坡的監(jiān)測數(shù)據(jù)挖掘 279
14.1 寶塔滑坡 279
14.2 滑坡監(jiān)測數(shù)據(jù)挖掘的可行性 280
14.3 寶塔滑坡形變監(jiān)測數(shù)據(jù)挖掘的視角 283
14.4 同點(diǎn)異時(shí)同向的視角挖掘 287
14.5 異點(diǎn)同時(shí)同向的視角挖掘 293
14.6 異點(diǎn)異時(shí)同向的視角挖掘 298
14.7 基于數(shù)據(jù)場的例外挖掘 301
14.8 寶塔滑坡形變監(jiān)測的知識(shí)及討論 305
第15章 GIS數(shù)據(jù)挖掘 311
15.1 空間關(guān)聯(lián)規(guī)則挖掘 311
15.2 基于歸納學(xué)習(xí)挖掘空間分布規(guī)則 320
15.3 基于粗集發(fā)現(xiàn)決策知識(shí) 324
15.4 空間聚類知識(shí)挖掘 331
參考文獻(xiàn) 342
后記 352
Contents
Foreword on the book series
Foreword I
Foreword II
Foreword III
Foreword IV
Preface of the 3rd edition
Preface of the 2nd edition
Preface of the 1st edition
Chapter 1 Introduction 1
1.1 The origin of spatial data mining 1
1.2 The value from spatial data mining 8
1.3 Problems in spatial data mining 10
1.4 Content and organization of the book 13
Chapter 2 Principles of Spatial Data Mining 16
2.1 Fundamental definitions and properties 16
2.2 Spatial data mining pyramid 19
2.3 Spatial data mining views 23
2.4 Knowledge form of spatial data mining 30
2.5 Representation of spatial knowledge 33
2.6 Relationship between spatial data mining and related disciplines 35
Chapter 3 Data Sources for Spatial Data Mining 39
3.1 Contents and characteristics of spatial data 39
3.2 Spatial data acquisition 42
3.3 Spatial data structure 44
3.4 Spatial data model 47
3.5 Organization and management of spatial data 52
3.6 National Spatial Data Infrastructure 57
3.7 Chinese National Spatial Data Infrastructure 62
3.8 From spatial data infrastructure to big data 66
Chapter 4 Spatial Data Cleaning 71
4.1 Pollution of spatial data 71
4.2 Basic contents of spatial data cleaning 76
4.3 Cleaning spatial observed data 78
4.4 Cleaning remote sensing images 94
4.5 Spatial data selection with DHPmethod 107
Chapter 5 Theoretical Methods Available for Spatial Data Mining 112
5.1 Crisp set theory 112
5.2 Extended set theory 118
5.3 Bionics 122
5.4 Knowledge Map 127
5.5 Visualization 127
5.6 Spatial data mining system 128
Chapter 6 Data Field 132
6.1 Data radiation 132
6.2 Concept and nature of the data field 135
6.3 Field strength and potential of the data field 139
Chapter 7 Cloud Model 147
7.1 Randomness and fuzziness 147
7.2 Concept of the cloud model 148
7.3 Types of cloud model 151
7.4 Cloud generator 155
7.5 Cloud transformation 161
7.6 Uncertain reasoning with cloud model 163
Chapter 8 Geo-rough space and conceptual lattice 166
8.1 The connotation of geo-rough space 166
8.2 Extension of geo-rough space in geoscience 176
8.3 Concept lattice and its nature 178
8.4 Construction of concept lattice 185
Chapter 9 Intelligent Retrieval for Remote Sensing Image 196
9.1 Image retrieval features 196
9.2 Remote sensing image statistical search 199
9.3 Remote sensing image deep retrieval 206
Chapter 10 Classification for Remote Sensing Image 212
10.1 Image classification based on inductive learning and Bayesian methods 212
10.2 Image classification based on cloud model 216
10.3 Image classification based on rough neural network 218
10.4 Thematic extraction based on geo-rough space 219
Chapter 11 Change Detection for Remote Sensing Image 221
11.1 Methodology for change detection 221
11.2 Image change detection with object-oriented machine learning 224
11.3 Sequential change detection under spatial characteristics 230
11.4 Dynamic target tracking based on drone video 237
Chapter 12 Video Data Mining in Time and Space Distribution 244
12.1 Smart compression and cloud storage for video data 244
12.2 Content-based video data retrieval 245
12.3 Spatiotemporal video data mining 247
12.4 Video face super-resolution recognition and expression mining 249
12.5 Satellite video coding based on long-range background dictionary 262
Chapter 13 Remote Sensing Nightlight Image Mining 267
13.1 Necessity of remote sensing nightlight image mining 267
13.2 China regional economic analysis 268
13.3 “One Belt, One Road” urban development assessment 271
13.4 Humanitarian disaster assessment 274
Chapter 14 Monitoring Data Mining of Baota Landslide 279
14.1 Baota Landslide 279
14.2 Feasibility of data mining for landslide monitoring 280
14.3 Mining views for Baota landslide deformation monitoring data 283
14.4 Mining with the same point, different time, the same direction 287
14.5 Mining with different point, the same time, the same direction 293
14.6 Mining with different point, different time, the same direction 298
14.7 Data field based exception mining 301
14.8 Knowledge and discussion on the deformation monitoring of Baota landslide 305
Chapter 15 GIS Data Mining 311
15.1 Mining spatial association rules 311
15.2 Rules for spatial distribution based on inductive learning 320
15.3 Finding decisions based on rough sets 324
15.4 Spatial clustering knowledge discovery 331
References 342
Postscript 352