Deep Learning Spatial-Spectral Processing of Hyperspectral Images for Pigment Mapping of Cultural Heritage Artifacts

2021 ◽  
pp. 200-214
Author(s):  
Di Bai ◽  
David W. Messinger ◽  
David Howell
Keyword(s):  
Deep Learning ◽  
Cultural Heritage ◽  
Hyperspectral Images ◽  
Spectral Processing ◽  
Pigment Mapping

scholarly journals Technology-Powered Strategies to Rethink the Pedagogy of History and Cultural Heritage through Symmetries and Narratives

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 367 ◽  
Author(s):  
Martín López-Nores ◽  
Omar Bravo-Quezada ◽  
Maddalena Bassani ◽  
Angeliki Antoniou ◽  
Ioanna Lykourentzou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Web ◽  
Cultural Heritage ◽  
Digital Humanities ◽  
Teaching And Learning ◽  
Computational Analysis ◽  
Learning Technologies ◽  
Cross Border ◽  
New Information ◽  
New Strategies

Recent advances in semantic web and deep learning technologies enable new means for the computational analysis of vast amounts of information from the field of digital humanities. We discuss how some of the techniques can be used to identify historical and cultural symmetries between different characters, locations, events or venues, and how these can be harnessed to develop new strategies to promote intercultural and cross-border aspects that support the teaching and learning of history and heritage. The strategies have been put to the test in the context of the European project CrossCult, revealing enormous potential to encourage curiosity to discover new information and increase retention of learned information.

scholarly journals Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

2018 ◽  
Vol 10 (11) ◽  
pp. 1827 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi ◽  
Youkyung Han ◽  
Yongil Kim
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Spatial Information ◽  
Short Term Memory ◽  
Hyperspectral Images ◽  
Convolutional Network ◽  
Ground Truth Data ◽  
Fully Convolutional Network ◽  
Training Samples ◽  
Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

Tongue tumor detection in hyperspectral images using deep learning semantic segmentation

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Stojan Trajanovski ◽  
Caifeng Shan ◽  
Pim J.C. Weijtmans ◽  
Susan G. Brouwer de Koning ◽  
Theo J. M. Ruers
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Tumor Detection ◽  
Hyperspectral Images ◽  
Tongue Tumor

Deep Learning-Based Enhancement of Hyperspectral Images Using Simulated Ground Truth

2018 ◽  
Author(s):  
Artem Nikonorov ◽  
Maksim Petrov ◽  
Sergey Bibikov ◽  
Viktoria Kutikova ◽  
Pavel Yakimov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ground Truth ◽  
Hyperspectral Images

Efficient classification of the hyperspectral images using deep learning

2018 ◽  
Vol 77 (20) ◽  
pp. 27061-27074 ◽  
Author(s):  
Simranjit Singh ◽  
Singara Singh Kasana
Keyword(s):  
Deep Learning ◽  
Hyperspectral Images

Deep learning to detect built cultural heritage from satellite imagery. - Spatial distribution and size of vernacular houses in Sumba, Indonesia -

2021 ◽  
Vol 52 ◽  
pp. 171-183
Author(s):  
Fabrice Monna ◽  
Tanguy Rolland ◽  
Anthony Denaire ◽  
Nicolas Navarro ◽  
Ludovic Granjon ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Deep Learning ◽  
Cultural Heritage ◽  
Satellite Imagery

Performance Evaluation of Classical Classifiers and Deep Learning Approaches for Polymers Classification Based on Hyperspectral Images

2021 ◽  
pp. 281-292
Author(s):  
Javier Lorenzo-Navarro ◽  
Silvia Serranti ◽  
Giuseppe Bonifazi ◽  
Giuseppe Capobianco
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Hyperspectral Images ◽  
Learning Approaches
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