Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings

• URL: http://arxiv.org/abs/2103.10614v1
• Date: Fri, 19 Mar 2021 03:32:28 GMT
• Title: Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings
• Authors: Zhongyang Zhang, Zhiyang Xu, Zia Ahmed, Asif Salekin, Tauhidur Rahman
• Abstract summary: Hyperspectral images (HSIs) with narrow spectral bands can capture rich spectral information, making them suitable for many computer vision tasks. One of the fundamental limitations of HSI is its low spatial resolution, and several recent works on super-resolution(SR) have been proposed to tackle this challenge. We propose a Meta-Learning-Based Super-Resolution(MLSR) model, which can take in HSI images at an arbitrary number of input bands' peak wavelengths and generate super-resolved HSIs with an arbitrary number of output bands' peak wavelengths.
• Score: 1.2148588577240576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral images (HSIs) with narrow spectral bands can capture rich spectral information, making them suitable for many computer vision tasks. One of the fundamental limitations of HSI is its low spatial resolution, and several recent works on super-resolution(SR) have been proposed to tackle this challenge. However, due to HSI cameras' diversity, different cameras capture images with different spectral response functions and the number of total channels. The existing HSI datasets are usually small and consequently insufficient for modeling. We propose a Meta-Learning-Based Super-Resolution(MLSR) model, which can take in HSI images at an arbitrary number of input bands' peak wavelengths and generate super-resolved HSIs with an arbitrary number of output bands' peak wavelengths. We artificially create sub-datasets by sampling the bands from NTIRE2020 and ICVL datasets to simulate the cross-dataset settings and perform HSI SR with spectral interpolation and extrapolation on them. We train a single MLSR model for all sub-datasets and train dedicated baseline models for each sub-dataset. The results show the proposed model has the same level or better performance compared to the-state-of-the-art HSI SR methods.

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