Hyperspectral Super-Resolution with Spectral Unmixing Constraints

Charis Lanaras,Emmanuel Baltsavias,Konrad Schindler

doi:10.3390/rs9111196

Abstract

Hyperspectral sensors capture a portion of the visible and near-infrared spectrum with many narrow spectral bands. This makes it possible to better discriminate objects based on their reflectance spectra and to derive more detailed object properties. For technical reasons, the high spectral resolution comes at the cost of lower spatial resolution. To mitigate that problem, one may combine such images with conventional multispectral images of higher spatial, but lower spectral resolution. The process of fusing the two types of imagery into a product with both high spatial and spectral resolution is called hyperspectral super-resolution. We propose a method that performs hyperspectral super-resolution by jointly unmixing the two input images into pure reflectance spectra of the observed materials, along with the associated mixing coefficients. Joint super-resolution and unmixing is solved by a coupled matrix factorization, taking into account several useful physical constraints. The formulation also includes adaptive spatial regularization to exploit local geometric information from the multispectral image. Moreover, we estimate the relative spatial and spectral responses of the two sensors from the data. That information is required for the super-resolution, but often at most approximately known for real-world images. In experiments with five public datasets, we show that the proposed approach delivers up to 15% improved hyperspectral super-resolution.

Highlights

Hyperspectral imaging, called imaging spectroscopy, delivers image data with many contiguous spectral bands of narrow bandwidth, on the order of a few nm
To achieve an acceptable signal-to-noise ratio (SNR), the sensor area per pixel must be large, leading to coarser geometric resolution. This is the opposite situation of multispectral cameras, where much of the spectral information is sacrificed to achieve high spatial resolution, by integrating the scene radiance over fewer, much wider spectral bands
Complementary tables and figures are available in the Supplementary Materials

Summary

Introduction

Hyperspectral imaging, called imaging spectroscopy, delivers image data with many (up to several hundreds) contiguous spectral bands of narrow bandwidth, on the order of a few nm. One cannot separate two physically-different surface compounds if they always occur together in fixed proportions Based on these spectra, one can detect and localize more different objects, e.g., for land cover mapping, environmental and agricultural monitoring; one can estimate further (bio-)physical and (bio-)chemical properties. To achieve an acceptable signal-to-noise ratio (SNR), the sensor area per pixel must be large, leading to coarser geometric resolution. This is the opposite situation of multispectral cameras, where much of the spectral information is sacrificed to achieve high spatial resolution, by integrating the scene radiance over fewer, much wider spectral bands

Results

Discussion

Conclusion