For more than three decades, geologists have been using passive remotely sensed data, both multispectral and hyperspectral, for geological applications such as mapping, structural interpretation, pollution and mine tailings, prospecting for Earth mineral resources as well as planetary geology.
Since its beginning, spaceborne multispectral imaging has provided continuous full global coverage. The significant advantages of multispectral imaging are the continuous wide area coverage in connection with long-term availability as well as the reduced level of complexity and computational requirements for data processing. The launching of new satellite missions, such as Sentinel-2, Sentinel-3 and Landsat 8 OLI, reflects the continuous interest on this type of data.
On the other hand, over the last two decades the advent of high spectral resolution imaging (spaceborne, airborne sensors and ground cameras), rooted in technological, modeling and processing advances, has opened a new era in geological applications. In fact, the very high spectral resolution of hyperspectral cubes, offers unprecedented capabilities in the identification and quantification of materials and their physical/chemical properties based on their unique spectral signatures, both in Earth and planetary exploration. Consequently, this led to the development of a new suite of advanced processing techniques
based on imaging spectroscopy and machine learning for the detailed detection, classification, discrimination, identification, characterization, and quantification of materials and their properties.
This Special Issue aims at collecting high-level contributions focusing on new advances in multispectral and hyperspectral imaging and relative processing algorithms for geological applications.
More specifically, it will address topics included in the following non-exhaustive list of geological applications and relative data processing techniques/algorithms:
- Retrieval of surface composition: lithological and mineral mapping
- Mapping of alteration zones and associated metal deposits (including Rare Earth Elements and minerals)
- Planetary geology – Surface mineralogy and composition (e.g. Mars, Moon etc)
- Geochemical studies
- Hydrocarbon exploration
- Mineral chemistry and spectroscopy
- Mine tailings and pollution detection
- Drill core imaging
- Ground-based outcrop hyperspectral imagin
- Multiscale imaging spectroscopy
Data processing techniques/algorithms:
- Data preprocessing (e.g. for atmospheric corrections, noise reduction, data gap filling, stripping, image enhancement etc)
- Imaging spectroscopy – analysis of spectral features of minerals and rocks
- Classification (including classic tools, such as Bayesian classification, forest trees and more advanced tools, such as conventional and Deep Neural Networks, Support Vector Machines etc)
- Clustering (including classic and more advanced tools such as Subspace Clustering, Clustering Ensemble etc)
- Spectral unmixing adopting either linear or non-linear models, and using Bayesian or nonBayesian approaches for parameter estimation
- Dimensionality reduction
- Data transformations (e.g. Fourier transform, wavelet transform etc)
- Validation procedures
- Data fusion