Hyperspectral image analysis : advances in machine learning and signal processing /: advances in machine learning and signal processing. (2020)

• Record Type: Book
• Further Information: Note: Saurabh Prasad, Jocelyn Chanussot, editors.
• Other Names: Prasad, Saurabh, 1980-; Chanussot, Jocelyn
• Contents: Intro -- Contents -- 1 Introduction -- 2 Machine Learning Methods for Spatial and Temporal Parameter Estimation -- 2.1 Introduction -- 2.1.1 Remote Sensing as a Diagnostic Tool -- 2.1.2 Data and Model Challenges -- 2.1.3 Goals and Outline -- 2.2 Gap Filling and Multi-sensor Fusion -- 2.2.1 Proposed Approach -- 2.2.2 LMC-GP -- 2.2.3 Data and Setup -- 2.2.4 Results -- 2.3 Distribution Regression for Multiscale Estimation -- 2.3.1 Kernel Distribution Regression -- 2.3.2 Data and Setup -- 2.3.3 Results -- 2.4 Global Parameter Estimation in the Cloud -- 2.4.1 Data and Setup -- 2.4.2 Results 2.5 Conclusions -- References -- 3 Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms -- 3.1 Introduction -- 3.1.1 History of Deep Learning in Computer Vision -- 3.1.2 History of Deep Learning for HSI Tasks -- 3.1.3 Challenges -- 3.2 Feed-Forward Neural Networks -- 3.2.1 Perceptron -- 3.2.2 Multi-layer Neural Networks -- 3.2.3 Learning and Gradient Computation -- 3.3 Deep Neural Networks -- 3.3.1 Autoencoders -- 3.3.2 Stacked Autoencoders -- 3.3.3 Recurrent Neural Networks -- 3.3.4 Long Short-Term Memory -- 3.4 Convolutional Neural Networks 3.4.1 Building Blocks of CNNs -- 3.4.2 CNN Flavors for HSI -- 3.5 Software Tools for Deep Learning -- 3.6 Conclusion -- References -- 4 Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine -- 4.1 Introduction -- 4.2 Applications of Hyperspectral Imaging -- 4.2.1 Remote Sensing Case Study: Urban Land Cover Classification -- 4.2.2 Biomedical Application: Tissue Histology -- 4.3 Practical Considerations and Related Work -- 4.3.1 Practical Considerations -- 4.3.2 Related Developments in the Community -- 4.4 Experimental Setup -- 4.4.1 CNNs 4.4.2 RNNs -- 4.4.3 CRNNs -- 4.5 Quantitative and Qualitative Results -- 4.5.1 Remote Sensing Results -- 4.5.2 Biomedical Results -- 4.5.3 Source Code and Data -- 4.6 Design Choices and Hyperparameters -- 4.6.1 Convolutional Layer Hyperparameters -- 4.6.2 Pooling Layer Hyperparameters -- 4.6.3 Training Hyperparameters -- 4.6.4 General Model Hyperparameters -- 4.6.5 Regularization Hyperparameters -- 4.7 Concluding Remarks -- References -- 5 Advances in Deep Learning for Hyperspectral Image Analysis-Addressing Challenges Arising in Practical Imaging Scenarios 5.1 Deep Learning-Challenges presented by Hyperspectral Imagery -- 5.2 Robust Learning with Limited Labeled Data -- 5.2.1 Unsupervised Feature Learning -- 5.2.2 Semi-supervised learning -- 5.2.3 Active learning -- 5.3 Knowledge Transfer Between Sources -- 5.3.1 Transfer Learning and Domain Adaptation -- 5.3.2 Transferring Knowledge-Beyond Classification -- 5.4 Data Augmentation -- 5.5 Future Directions -- References -- 6 Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 006.37; Hyperspectral imaging; Image processing; Hyperspectral imaging; Image processing; Electronic books
• Languages: English
• ISBNs: 9783030386177; 3030386171
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.