Deep Learning for Hyperspectral Image Analysis and Classification
Tao, Linmi.
Deep Learning for Hyperspectral Image Analysis and Classification [electronic resource] / by Linmi Tao, Atif Mughees. - 1st ed. 2021. - XII, 207 p. 121 illus., 106 illus. in color. online resource. - Engineering Applications of Computational Methods, 5 2662-3374 ; . - Engineering Applications of Computational Methods, 5 .
Introduction -- Hyperspectral Imaging System -- Classification Techniques for HSI -- Preprocessing: Noise Reduction/ Band Categorization for HSI -- Spatial Feature Extraction Using Segmentation -- Multiple Deep learning models for feature extraction in classification -- Deep learning for merging spatial and spectral information in classification -- Sparse cording for Hyperspectral Data -- Classification Applications of HSI classification -- Conclusion.
This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are theoriginal contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
9789813344204
10.1007/978-981-33-4420-4 doi
Machine learning.
Artificial intelligence.
Image processing--Digital techniques.
Computer vision.
Signal processing.
Machine Learning.
Artificial Intelligence.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer Vision.
Signal, Speech and Image Processing.
Q325.5-.7
006.31
Deep Learning for Hyperspectral Image Analysis and Classification [electronic resource] / by Linmi Tao, Atif Mughees. - 1st ed. 2021. - XII, 207 p. 121 illus., 106 illus. in color. online resource. - Engineering Applications of Computational Methods, 5 2662-3374 ; . - Engineering Applications of Computational Methods, 5 .
Introduction -- Hyperspectral Imaging System -- Classification Techniques for HSI -- Preprocessing: Noise Reduction/ Band Categorization for HSI -- Spatial Feature Extraction Using Segmentation -- Multiple Deep learning models for feature extraction in classification -- Deep learning for merging spatial and spectral information in classification -- Sparse cording for Hyperspectral Data -- Classification Applications of HSI classification -- Conclusion.
This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are theoriginal contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
9789813344204
10.1007/978-981-33-4420-4 doi
Machine learning.
Artificial intelligence.
Image processing--Digital techniques.
Computer vision.
Signal processing.
Machine Learning.
Artificial Intelligence.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer Vision.
Signal, Speech and Image Processing.
Q325.5-.7
006.31