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 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.