| 摘要 | 第4-6页 |
| Abstract | 第6-8页 |
| Abbreviations | 第19-20页 |
| Chapter 1 Introduction | 第20-49页 |
| 1.1 Overview on Remote Sensing | 第20-22页 |
| 1.2 Remote Sensing Imaging System | 第22-25页 |
| 1.2.1 Fundamental Concepts | 第22-23页 |
| 1.2.2 Hyper-spectral images | 第23-24页 |
| 1.2.3 VHR remote sensing images | 第24-25页 |
| 1.3 Overview on Remote Sensing Images Representation | 第25-41页 |
| 1.3.1 Low Level Features for VHR Images Representation | 第26-28页 |
| 1.3.2 Midlevel Features for VHR Images Representation | 第28-33页 |
| 1.3.3 High Level Features for VHR Images Representation | 第33-41页 |
| 1.4 Support Vector Machine for Remote Sensing Images Classification | 第41-44页 |
| 1.4.1 Fundamental Concept of SVM | 第42-43页 |
| 1.4.2 Multiclass classification based on SVM | 第43-44页 |
| 1.5 The Problems | 第44-45页 |
| 1.6 Thesis Contributions | 第45-47页 |
| 1.7 Structure of the Thesis | 第47-49页 |
| Chapter 2 A Sparse PCA-Based Features Selection Method for VHR Scene Classification | 第49-70页 |
| 2.1 Introduction | 第49-60页 |
| 2.1.1 s PCA Technique for Features Selection | 第51-54页 |
| 2.1.2 Experimental Results and Analysis | 第54-60页 |
| 2.2 Classification of VHR satellite imagery based on saliency computing and sparse PCA | 第60-69页 |
| 2.2.1 Saliency Detection for Patches Sampling | 第61-62页 |
| 2.2.2 Experimental Results | 第62-69页 |
| 2.3 Summary | 第69-70页 |
| Chapter 3 A Deep Feature Fusion Method for VHR Remote Sensing Scene Classification | 第70-92页 |
| 3.1 Introduction | 第70-72页 |
| 3.2 Deep Features Fusion Based On Discriminant Correlation Analysis Description | 第72-88页 |
| 3.2.1 Deep VGG-Net for Feature Extraction | 第73-74页 |
| 3.2.2 Features Fusion Based On Discriminant Correlation Analysis | 第74-78页 |
| 3.2.3 Experimental Results and Setup | 第78-88页 |
| 3.3 Experiment Result Deep Feature Extraction and Combination Based on pre-trained CNN Models | 第88-91页 |
| 3.4 Summary | 第91-92页 |
| Chapter 4 Discriminative DCNN Features Extraction for VHR Image Scene Classification | 第92-105页 |
| 4.1 Introduction | 第92-93页 |
| 4.2 Bag of Deep Features | 第93-104页 |
| 4.2.1 Bag of convolutional features Extraction | 第95页 |
| 4.2.2 Experimental Results of Bag of Deep Features | 第95-104页 |
| 4.3 Summary | 第104-105页 |
| Conclusions | 第105-107页 |
| References | 第107-120页 |
| List of Publications | 第120-123页 |
| Acknowledgements | 第123页 |
