Abstract
This article deals with the problem of improving the spatial resolution of hyperspectral (HS) data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) mission. For this purpose, higher spatial resolution data from the Sentinel-2 (S2) mission are exploited. Particularly, 10 S2 bands at 10 and 20 m spatial resolution are used to accomplish the PRISMA super-resolution (SR) task. The article presents a new end-to-end procedure, called PRISMA-SR, that starting from the S2 data and the low-resolution PRISMA image, provides a super-resolved image with a spatial resolution of 10 m and the same spectral resolution as the PRISMA HS sensor. The first step of the PRISMA-SR procedure consists in fusing S2 data at different spatial resolutions to obtain a synthetic MS image with 10 m spatial resolution and 10 spectral bands. Then, an unsupervised procedure is applied to coregister the fused S2 image and the PRISMA image. Finally, the two images at different spatial resolutions are properly combined in order to obtain the super-resolved HS image. Solutions for each step of the PRISMA-SR processing chain are proposed and discussed. Simulated data are used to show the effectiveness of the PRISMA-SR scheme and to investigate the impact on its performance of each step of the processing chain. Real S2 and PRISMA images are finally considered to provide an example of the application of the PRISMA-SR.
Highlights
HYPERSPECTRAL (HS) sensors offer the opportunity of analyzing the chemical and physical composition of the remotely sensed scene thanks to their ability of measuring the spectrum of the observed pixels in a large number of contiguous and narrow spectral channels ([1])
A new end-to-end procedure called PRISMASR has been presented to increase the spatial resolution of PRISMA hyperspectral data
The main idea behind the proposed procedure is that of exploiting S2 data acquired with spatial resolution of 10 m and 20 m to obtain a super-resolved PRISMA image with a pixel size of 10 m
Summary
HYPERSPECTRAL (HS) sensors offer the opportunity of analyzing the chemical and physical composition of the remotely sensed scene thanks to their ability of measuring the spectrum of the observed pixels in a large number of contiguous and narrow spectral channels ([1]). The first category includes methods obtained by adapting the techniques developed to fuse a high-resolution panchromatic (PAN) image with a lower resolution MS image (pansharpening, [12]) to the HS-MS fusion problem In this context, Selva et al ([13]) proposed a framework (called hypersharpening) that adapts pansharpening algorithms based on the Multi-Resolution Analysis (MRA, [12]) to HS-MS fusion. In the context of image fusion, in the last years, several Convolutional Neural Network (CNN) based algorithms have been proposed to approach the pansharpening problem ([26]- [28]). The method first initializes a high spatial resolution (HR) HS image from the model based fusion framework solving a Sylvester equation It derives an intermediate HR-HS image exploiting a CNN based on residual learning. A comprehensive review of the recent advances on CNN based HS-MS fusion methods can be found in [33]
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