Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial
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RESEARCH PAPER
Spectral enhancement of Landsat OLI images by using Hyperion data: a comparison between multilayer perceptron and radial basis function networks Mohammad Hossein Mokhtari 1
&
Kaveh Deilami 2 & Vahid Moosavi 3
Received: 12 August 2019 / Accepted: 13 February 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The deactivation of Earth Observing-1 satellite has resulted in the termination of capturing Hyperion data as a unique source of hyperspectral satellite imagery. These images also were collected through an on-demand service and thereby are not available for the entire Earth’s surface. The Operational Land Imager (OLI) sensor, on the other hand, provides a free source of multi-spectral images with global coverage. Recognized these facts, the aim of this paper is to enhance the spectral resolution of OLI images by using existing Hyperion imageries to generate a high spectral resolution image for a desired date and site. This was conducted through the artificial neural network (ANN). To find the suitable ANN, we compared the performance of multilayer perceptron (MLP) and radial basis function (RBF) networks for spectral enhancement. The research obtained two Hyperion and OLI images covering West Region of Tehran, Iran, on 4 January 2016. From 242 original Hyperion spectral bands, we selected 31 bands to reproduce from OLI spectral bands. These were determined through visual inspection, principal component analysis and Pearson’s correlation test. The MLP and RBF networks were generated based on the OLI bands 1–7 and per 31 Hyperion bands as input and output layers respectively. The comparison between the spectral bands of spectra-enhanced image and original Hyperion data indicated a good agreement (0.884 > R2 > 0.692). This study also found MLP network delivered higher accuracy against RBF network for spectral enhancement. The spectra-enhanced image can be used in studies with the need of images with continuous spectral bands. Keywords Hyperspectral . Artificial neural network . Spectral fusion . OLI . Hyperion . Spectral enhancement
Introduction Communicated by: H. Babaie * Mohammad Hossein Mokhtari [email protected] Kaveh Deilami [email protected] Vahid Moosavi [email protected] 1
Department of Desert Management, Faculty of Natural Resources, Yazd University, Yazd, Iran
2
Centre for Urban Research, School of Global, Urban and Social Studies, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
3
Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares, Tehran, Iran
Hyperion sensor captures the Earth’s surface through a total of 242 spectral bands at wavelength from 0.4 to 2.5 μm (μm) with a 30-m resolution (Barry 2001). As a result, the spectral coverage of a Hyperion image somehow represents the continuous spectral in nature. Such spectral thus is comparable to the spectral library generated in the laboratory. This specification has resulted into the unique application of Hyperion images in vario
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