Transformation of Multispectral Data to Quasi-Hyperspectral Data Using Convolutional Neural Network Regression

Hyperspectral (HS) data are proven to be more resourceful compared to multispectral (MS) data for object detection, classification, and several other applications. However, absence of any space-borne HS sensor since 2017, which can provide open-source data with global coverage, and high cost and lim...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 59; číslo 4; s. 3352 - 3368
Hlavní autoři:	Paul, Subir, Nagesh Kumar, D.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Agriculture Airborne remote sensing Airborne sensing Algorithms Artificial neural networks Artificial satellites Classification Convolutional neural network regression (CNNR) crop classification Data Data models Deep learning Earth Evaluation hyperspectral (HS) data Landsat Landsat satellites Machine learning Mathematical models Modelling multispectral (MS) data Neural networks Object recognition quasi-HS data Reflectance Reflectance curves Regression analysis Remote sensing Spatial discrimination learning Spatial resolution Spectral reflectance Statistical analysis Support vector machines Transformations
ISSN:	0196-2892, 1558-0644
On-line přístup:	Získat plný text
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