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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Published in:	IEEE transactions on geoscience and remote sensing Vol. 59; no. 4; pp. 3352 - 3368
Main Authors:	Paul, Subir, Nagesh Kumar, D.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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Abstract	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 limited obtainability of airborne sensors-based images limit the use of HS data. Transformation of readily available MS data into quasi-HS data can be a feasible solution for this issue. In this article, we propose the use of convolutional neural network regression (CNNR), a deep learning-based algorithm, for MS (i.e., Landsat 7/8) to quasi-HS (i.e., quasi-Hyperion) data transformation. The proposed CNNR model is compared with the existing pseudo-HS image transformation algorithm (PHITA), a simple linear model [i.e., stepwise linear regression (SLR)], and a nonlinear modeling approach [i.e., support vector regression (SVR)] by evaluating the quality of the quasi-Hyperion data. Contrary to these existing and simple models, the proposed CNNR model has the added advantage of utilizing deep learning-based spectral-spatial features for MS to quasi-HS data transformation through regression-based nonlinear modeling. Different statistical metrics are calculated to compare each band's reflectance values as well as spectral reflectance curve of each pixel of the quasi-Hyperion data with that of the original Hyperion data. The developed models and generated quasi-Hyperion data are also evaluated with application to crop classification. Analyzing the results of all the experiments, it is evident that CNNR model is more efficient compared to PHITA, SLR, and SVR in creating the quasi-Hyperion data and this transformed data are proven to be resourceful for crop classification application. The proposed CNNR model-based MS to quasi-HS data transformation approach can be used as a viable alternative for different applications in the absence of original HS images.
AbstractList	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 limited obtainability of airborne sensors-based images limit the use of HS data. Transformation of readily available MS data into quasi-HS data can be a feasible solution for this issue. In this article, we propose the use of convolutional neural network regression (CNNR), a deep learning-based algorithm, for MS (i.e., Landsat 7/8) to quasi-HS (i.e., quasi-Hyperion) data transformation. The proposed CNNR model is compared with the existing pseudo-HS image transformation algorithm (PHITA), a simple linear model [i.e., stepwise linear regression (SLR)], and a nonlinear modeling approach [i.e., support vector regression (SVR)] by evaluating the quality of the quasi-Hyperion data. Contrary to these existing and simple models, the proposed CNNR model has the added advantage of utilizing deep learning-based spectral–spatial features for MS to quasi-HS data transformation through regression-based nonlinear modeling. Different statistical metrics are calculated to compare each band’s reflectance values as well as spectral reflectance curve of each pixel of the quasi-Hyperion data with that of the original Hyperion data. The developed models and generated quasi-Hyperion data are also evaluated with application to crop classification. Analyzing the results of all the experiments, it is evident that CNNR model is more efficient compared to PHITA, SLR, and SVR in creating the quasi-Hyperion data and this transformed data are proven to be resourceful for crop classification application. The proposed CNNR model-based MS to quasi-HS data transformation approach can be used as a viable alternative for different applications in the absence of original HS images.
Author	Nagesh Kumar, D. Paul, Subir
Author_xml	– sequence: 1 givenname: Subir orcidid: 0000-0001-5651-2775 surname: Paul fullname: Paul, Subir email: subir.paul.iisc@gmail.com organization: Department of Civil Engineering, Indian Institute of Science, Bengaluru, India – sequence: 2 givenname: D. orcidid: 0000-0002-5294-8501 surname: Nagesh Kumar fullname: Nagesh Kumar, D. email: nagesh@iisc.ac.in organization: Department of Civil Engineering, Indian Institute of Science, Bengaluru, India
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SubjectTerms	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
Title	Transformation of Multispectral Data to Quasi-Hyperspectral Data Using Convolutional Neural Network Regression
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