Independent macroscopic chemical mappings of cultural heritage materials with reflectance imaging spectroscopy: case study of a 16thcentury Aztec manuscript

2017 ◽  
Vol 9 (42) ◽  
pp. 5997-6008 ◽  
Author(s):  
Fabien Pottier ◽  
Salomon Kwimang ◽  
Anne Michelin ◽  
Christine Andraud ◽  
Fabrice Goubard ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Spectral Feature ◽  
Image Data ◽  
Painting Materials ◽  
Hyperspectral Image Data ◽  
Reflectance Imaging Spectroscopy

Hyperspectral image data processing based on specific spectral feature extraction protocols allows mapping of historical painting materials independently.

scholarly journals Multi-scale guided feature extraction and classification algorithm for hyperspectral images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shiqi Huang ◽  
Ying Lu ◽  
Wenqing Wang ◽  
Ke Sun
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Multi Scale ◽  
Hyperspectral Image Data ◽  
Guided Filtering

AbstractTo solve the problem that the traditional hyperspectral image classification method cannot effectively distinguish the boundary of objects with a single scale feature, which leads to low classification accuracy, this paper introduces the idea of guided filtering into hyperspectral image classification, and then proposes a multi-scale guided feature extraction and classification (MGFEC) algorithm for hyperspectral images. Firstly, the principal component analysis theory is used to reduce the dimension of hyperspectral image data. Then, guided filtering algorithm is used to achieve multi-scale spatial structure extraction of hyperspectral image by setting different sizes of filtering windows, so as to retain more edge details. Finally, the extracted multi-scale features are input into the support vector machine classifier for classification. Several practical hyperspectral image datasets were used to verify the experiment, and compared with other spectral feature extraction algorithms. The experimental results show that the multi-scale features extracted by the MGFEC algorithm proposed in this paper are more accurate than those extracted by only using spectral information, which leads to the improvement of the final classification accuracy. This fully shows that the proposed method is not only effective, but also suitable for processing different hyperspectral image data.

scholarly journals PROCESSING CHAINS FOR DESIS AND ENMAP IMAGING SPECTROSCOPY DATA: SIMILARITIES AND DIFFERENCES

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 177-180
Author(s):  
T. Storch ◽  
R. Müller
Keyword(s):  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Image Data ◽  
Calibration Data ◽  
Imaging Spectrometer ◽  
Environmental Mapping ◽  
Hyperspectral Image Data ◽  
Resolution Imaging ◽  
Similarities And Differences ◽  
German Aerospace

The Earth Observation Center (EOC) of the German Aerospace Center (DLR) realizes operational processors for DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program) high-resolution imaging spectroscopy remote sensing satellite missions. DESIS is planned to be launched in 2018 and EnMAP in 2020. The developmental (namely schedule, deployment, and team) and functional (namely processing levels, algorithms in processors, and archiving approaches) similarities and differences of the fully-automatic processors are analyzed. The processing chains generate high-quality standardized image products for users at different levels taking characterization and calibration data into account. EOC has long lasting experiences with the airborne and spaceborne acquisition, processing, and analysis of hyperspectral image data. It turns out that both activities strongly benefit from each other.

Weighting hyperspectral image data for improved multivariate curve resolution results

2008 ◽  
Vol 22 (9) ◽  
pp. 482-490 ◽  
Author(s):  
Howland D. T. Jones ◽  
David M. Haaland ◽  
Michael B. Sinclair ◽  
David K. Melgaard ◽  
Mark H. Van Benthem ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Image Data ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Hyperspectral Image Data
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