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Table of contents
I Introduction to land cover maps
1 The operational production of land cover maps
1.1 Land cover maps
1.2 Remote sensing from space
1.3 A classification task
1.3.1 Photo-interpretation
1.3.2 A set of decision rules
1.3.3 Supervised classification with machine learning
1.4 Definition of the problem and main research objectives
1.4.1 The OSO map
1.4.2 The importance of context in high-resolution image classification
1.4.3 Challenges of a large scale production
1.4.4 Objectives and scope
2 Operational optical imaging systems for land cover mapping at a global scale
2.1 Properties of the Sentinel-2 constellation
2.2 Properties of SPOT-7
3 Production of the OSO land cover map
3.1 Reference data and sample selection
3.1.1 Data sources
3.1.2 Split of training and evaluation sets
3.2 Cloud-filling and temporal interpolation
3.3 Feature extraction
3.4 Details of the supervised learning algorithm: Random Forest
3.4.1 Purity criteria
3.4.2 Ensemble methods
3.5 The final prediction phase
3.5.1 Eco-climatic stratification
3.5.2 Tile-based classification and mosaicking
3.5.3 Validation
II Basics of contextual classification
4 Defining the spatial support
4.1 Sliding windows
4.2 Objects from an image segmentation
4.2.1 Object Based Image Analysis (OBIA)
4.2.2 Mean Shift segmentation algorithm
4.3 Superpixels
4.4 Multi-scale representations
4.5 Overview of the spatial supports
5 Contextual features
5.1 Isotropic features
5.1.1 Local statistics: the sample mean and variance
5.1.2 Structured texture filters
5.2 Oriented texture filters
5.2.1 Describing oriented repeatability
5.2.2 Local binary patterns
5.3 Key-point based methods
5.4 Level set methods
5.5 Shape features
5.6 Overview
6 Evaluation of land cover maps
6.1 Class accuracy metrics
6.2 Standard geometric quality metrics
6.3 Pixel Based Corner Match
6.3.1 Corner detection
6.3.2 Corner matching
6.3.3 Impact of regularization
6.3.4 Calibration of the metric
6.3.5 Further validation with dense reference data
III Advanced contextual classification
7 Scaling the spatial supports
7.1 Application of Mean Shift to large images
7.2 Scaling the SLIC superpixel algorithm
7.2.1 Segmentation quality criteria
7.2.2 Tile-wise processing procedure
7.2.3 Parallel processing
7.2.4 Estimating the optimal tiling parameters
7.2.5 Experimental results
7.2.6 Overview and validation
8 Stacked contextual classification methods
8.1 Using the prediction of nearby pixels
8.1.1 Bag of Visual Words
8.1.2 Random Fields
8.1.3 Stacked classifiers
8.1.4 Semantic Texton Forests
8.1.5 Auto-Context
8.1.6 Summary of the literature
8.2 Histogram of Auto-Context Classes in Superpixels
8.2.1 Principle of HACCS
8.2.2 Illustrations
8.3 Basic Semantic Texton Forest
8.4 Overview with regards to operational land cover mapping
9 Deep Learning on images with Convolutional Neural Networks
9.1 What is Deep Learning ?
9.1.1 The Neural Network, a connected group of simple neurons
9.1.2 Convolutional Neural Networks
9.2 Deep Learning for land cover mapping
9.2.1 Patch-based network
9.2.2 Fully Convolutional Networks
9.2.3 Issues with sparse data
IV Results
10 Multispectral time series experiments on Sentinel-2 images
10.1 Experimental setup
10.2 Results of image-based contextual features
10.2.1 Experiments on T31TCJ
10.2.2 Experiments on the 11 tiles
10.2.3 Overview of the results
10.3 Results of semantic contextual features
10.3.1 Experiments on T31TCJ
10.3.2 Experiments on the 11 tiles
10.3.3 Overview of the results
10.4 Conclusions
11 Mono-date VHSR experiments
11.1 Experimental setup
11.2 Results
11.3 Conclusions
V Conclusion
12 Conclusion
12.1 The importance of contextual information
12.2 Different ways of including context
12.3 Overview of the experimental results
12.4 Perspectives
VI Appendices
