Spectral-spatial classification method

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Table of contents

1 Introduction to hyperspectral image classification 
1.1 Hyperspectral imaging
1.1.1 Light-matter interaction
1.1.2 Definitions
1.1.3 Acquisition
1.2 Supervised classification
1.2.1 Definitions and hypotheses
1.2.2 Generative classifiers
1.2.3 Discriminative model
1.2.4 Multi-class
1.2.5 The Kernel Trick
1.2.6 Training and assessing a classifier performance
1.3 Classification issues with HS data
1.3.1 Problems with the spectral dimension
1.3.2 Using spatial information
1.3.3 Obtaining reflectance images
1.4 Conclusion
2 State-of-the-art 
2.1 Introduction
2.2 Dealing with the high-dimensionality of spectral data
2.2.1 Unsupervised approaches
2.2.2 PLS-like approaches
2.2.3 FDA-like approaches
2.3 Using spatial information: Spectral-spatial approaches
2.3.1 Spatial information as an input parameter
2.3.2 Spatial information at the classification decision stage
2.3.3 Spatial information as a post-processing stage
2.4 Reflectance correction
2.4.1 Background
2.4.2 Physics-based transfer (model-based) correction
2.4.3 Scene-based correction
2.4.4 Image-based correction
2.5 Conclusion
3 Proposed approaches
3.1 Introduction
3.2 Dimension reduction
3.2.1 Prerequisites
3.2.2 Subspace decomposition: problem statement
3.2.3 Variability decomposition in RN and RP
3.2.4 DROP-D
3.2.5 DROP-D algorithm
3.3 Spectral-spatial
3.3.1 Construction of the score image
3.3.2 Anisotropic regularization
3.3.3 Score image regularization
3.4 Reflectance correction
3.4.1 Hypotheses
3.4.2 Lambertian hypothesis
3.4.3 Discrimination model hypothesis
3.4.4 Problem statement
3.4.5 Translation estimation
4 Experimental Results 
4.1 Data sets
4.1.1 Data set A: Proximal detection
4.1.2 Data set B: Remote-sensing
4.1.3 Performance measurements
4.2 Dimension reduction
4.2.1 Collinearity in RP
4.2.2 E↵ect of removing W on the class separability
4.2.3 Model calibration
4.2.4 Classification performances
4.3 Spatial regularization
4.3.1 Validation of the approach
4.3.1.1 On ‘what’ to apply the regularization
4.3.1.2 ‘When’ to apply regularization
4.3.2 Tuning robustness
4.3.3 E↵ect on score versus spatial
4.3.4 Classification results
4.3.5 Comparison with other approaches
4.4 Reflectance correction
4.4.1 Reflectance correction e↵ect on the reduced scores
4.4.2 Using log-radiance image for classification
4.4.3 Translation estimation
5 Conclusions and future work 
5.1 Conclusions
5.2 Future work
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