SEM-EDX hyperspectral data analysis for the study of soil aggregates

Geoderma ◽  
2022 ◽  
Vol 406 ◽  
pp. 115540
Author(s):  
Ignazio Allegretta ◽  
Stijn Legrand ◽  
Matthias Alfeld ◽  
Concetta Eliana Gattullo ◽  
Carlo Porfido ◽  
...  
Keyword(s):  
Data Analysis ◽  
Soil Aggregates ◽  
Hyperspectral Data

Deriving winter wheat characteristics from combined radar and hyperspectral data analysis

2006 ◽  
Author(s):  
Wolfgang Koppe ◽  
Rainer Laudien ◽  
Martin L. Gnyp ◽  
Liangliang Jia ◽  
Fei Li ◽  
...  
Keyword(s):  
Data Analysis ◽  
Winter Wheat ◽  
Hyperspectral Data

scholarly journals DCT-Based Preprocessing Approach for ICA in Hyperspectral Data Analysis

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1138 ◽  
Author(s):  
◽  
◽  
Keyword(s):  
Data Analysis ◽  
Hyperspectral Data

Use of filter vectors in hyperspectral data analysis

1995 ◽  
Author(s):  
Jeffrey H. Bowles ◽  
Peter J. Palmadesso ◽  
John A. Antoniades ◽  
Mark M. Baumback ◽  
Lee J. Rickard
Keyword(s):  
Data Analysis ◽  
Hyperspectral Data

scholarly journals Depth and Width Changeable Network-Based Deep Kernel Learning-Based Hyperspectral Sensor Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Liu ◽  
Tingting Wang ◽  
Yulong Qiao
Keyword(s):  
Data Analysis ◽  
Network Architecture ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Kernel Learning ◽  
Discriminative Ability ◽  
Neural Network Learning ◽  
And Performance ◽  
Hyperspectral Sensor

Sensor data analysis is used in many application areas, for example, Artificial Intelligence of Things (AIoT), with the rapid developing of the deep neural network learning that promotes its application area. In this work, we propose the Depth and Width Changeable Deep Kernel Learning-based hyperspectral sensing data analysis algorithm. Compared with the traditional kernel learning-based hyperspectral data classification, the proposed method has its advantages on the hyperspectral data classification. With the deep kernel learning, the feature is mapped through many times mapping and has the more discriminative ability. So, the deep kernel learning has the better performance compared with the multiple kernels learning. And it has the ability to adjust the network architecture for hyperspectral data space, with the optimization equation of the span bound. The experiments are implemented to testified the feasibility and performance of the algorithms on the hyperspectral data analysis, with the classification accuracy of hyperspectral data. The comprehensive analysis of the experiments shows that the proposed algorithm is feasible to hyperspectral sensor data analysis and its promising classification method in many areas data analysis.

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