Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images

Naoto Yokoya,Karl Segl,Jonathan Chan

doi:10.3390/rs8030172

Abstract

Spaceborne hyperspectral images are useful for large scale mineral mapping. Acquired at a ground sampling distance (GSD) of 30 m, the Environmental Mapping and Analysis Program (EnMAP) will be capable of putting many issues related to environment monitoring and resource exploration in perspective with measurements in the spectral range between 420 and 2450 nm. However, a higher spatial resolution is preferable for many applications. This paper investigates the potential of fusion-based resolution enhancement of hyperspectral data for mineral mapping. A pair of EnMAP and Sentinel-2 images is generated from a HyMap scene over a mining area. The simulation is based on well-established sensor end-to-end simulation tools. The EnMAP image is fused with Sentinel-2 10-m-GSD bands using a matrix factorization method to obtain resolution-enhanced EnMAP data at a 10 m GSD. Quality assessments of the enhanced data are conducted using quantitative measures and continuum removal and both show that high spectral and spatial fidelity are maintained. Finally, the results of spectral unmixing are compared with those expected from high-resolution hyperspectral data at a 10 m GSD. The comparison demonstrates high resemblance and shows the great potential of the resolution enhancement method for EnMAP type data in mineral mapping.

Highlights

Hyperspectral images provide important information for the characterization of lithology.Using hundreds of contiguous spectral bands with a narrow bandwidth, a pixel spectrum is generated for the potential discrimination of target minerals [1,2]
Among the hyperspectral and multispectral image fusion methods, we have focused on a matrix factorization method that requires relative sensor configurations, such as spectral response functions (SRFs) and point spread functions (PSFs), but produces good results with only a moderate computational burden [14]
We focus on Coupled nonnegative matrix factorization (CNMF) owing to its straightforward interpretation, robust performance as a benchmark method as shown in the literature, and its applicability to the fusion of spectrally non-overlapping images, which will be tackled in this paper

Summary

Introduction

Hyperspectral images provide important information for the characterization of lithology. Using hundreds of contiguous spectral bands with a narrow bandwidth, a pixel spectrum is generated for the potential discrimination of target minerals [1,2]. Spectral unmixing is an important technique for discerning minerals at a subpixel scale using hyperspectral image data. Geological mapping and mineral exploration with hyperspectral images have been studied for decades [3,4,5]. The shortwave infrared (SWIR) spectral range of 2.0–2.5 μm is considered to have the most important diagnostic absorption features, which are related to the vibration processes in atomic bonds. Numerous distinctive minerals associated with primary lithologies and with various alteration styles can be identified; for instance, Al–OH at 2.2 μm, Mg–OH at 2.3 μm, and Ca–CO3 at 2.32–2.35 μm [6]

Methods

Results

Conclusion