Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms

2020 ◽  
pp. 37-68
Author(s):  
Sebastian Berisha ◽  
Farideh Foroozandeh Shahraki ◽  
David Mayerich ◽  
Saurabh Prasad
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Image Analysis

scholarly journals Trends in Deep Learning for Medical Hyperspectral Image Analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Uzair Khan ◽  
Sidike Paheding ◽  
Colin Elkin ◽  
Vijay Devabhaktuni
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Image Analysis

scholarly journals An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

2021 ◽  
Vol 67 (2) ◽  
pp. 2393-2407
Author(s):  
J. Banumathi ◽  
A. Muthumari ◽  
S. Dhanasekaran ◽  
S. Rajasekaran ◽  
Irina V. Pustokhina ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Image Analysis

scholarly journals GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 61 ◽  
Author(s):  
Konstantinos Demertzis ◽  
Lazaros Iliadis
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Computational Cost ◽  
Training Methods ◽  
Training Time ◽  
Hyperspectral Image Analysis ◽  
First Time ◽  
Learning Architectures

Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above difficulties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it offers an improved training stability, high generalization performance and remarkable classification accuracy.

Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote  and Biomedicine

2020 ◽  
pp. 69-115
Author(s):  
Farideh Foroozandeh Shahraki ◽  
Leila Saadatifard ◽  
Sebastian Berisha ◽  
Mahsa Lotfollahi ◽  
David Mayerich ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Hyperspectral Image Analysis
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