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This paper describes the use of a new multi-image co-registration method tuned for SAR multi-temporal data collected with different orbits, viewing angles and polarization. Tests were performed using COSMO-SkyMed (X-band) and Sentinel-1 data (C-band) time series imaged in stripmap and spotlight modes. Results shows an overview sub-pixel accuracy also for the challenging co-registration of dual orbit image stacks (ascending vs. descending), where other methods fail.

Optical remote sensing is emerging among non-conventional geophysical methods for oil & gas exploration and mineral prospecting. Complementary to all traditional technologies such as seismic, magnetic, gravity or electric methods, multispectral imaging is able to detect long-term biochemical and geochemical environmental alterations, known as microseepage effect, produced by invisible small fluxes of light hydrocarbons migrating from the underground deposits to the surface. This paper describes a case study where satellite multispectral data were used to detect large-scale microseepage signals nearby Lake Turkana (Republic of Kenya). The satellite analysis highlighted the presence of invisible surface signals on top of several oilfields discovered only many years after the image collection.

Multispectral remote sensing is an emerging technology for the oil & gas industry. Since its first application, Earth Observation has seen an enormous breakthrough in a brand-new field such as geosciences for hydrocarbon exploration: both the awareness of the microseepage phenomenon and data processing methods for its detection have greatly improved in the last years. This paper describes a case study of microseepage signals detection in the East Africa Rift System, onshore of Lake Albert, using multi-sensor satellite time series. Results clearly show that the spectral anomalies identified from satellite are closely related to the known oilfields and that the microseepage maps can provide new high-quality data to reduce exploration risk.

This paper investigates the use of Minimum Noise Fraction (MNF) components to improve the spectral separability of two specific thematic classes in airborne hyperspectral imagery using Spectral Angle Mapper (SAM). Particularly, we compared trends on data distribution before and after MNF transform. Two different data sets recorded with the Multispectral Infrared Visible Imaging Spectrometer (MIVIS) were analyzed. In the first case study, the classification of MNF-transformed data led to an overall enhancement in mapping asbestos roofs. In the second case study, the classification of MNF-transformed data succeeded to distinguish between two different artificial lakes, whereas classification of original hyperspectral data failed. Overall, this study showed how the use of MNF as pre-processing could improve the capability to extract information from two different airborne hyperspectral data sets.

This paper describes the use of airborne hyperspectral remote sensing for mapping asbestos roofs in an orographic complex area in Northern Italy, the Aosta Valley. Using training samples collected during field surveys, thematic classification was able to detect the majority of asbestos surfaces. Considering the total amount of asbestos areas validation showed a correct detection of about 80%, while considering the number of asbestos roofs correctly detected this value decreased to 43%. This difference pointed out a clear relationship between data spatial resolution and asbestos roofs area. The study served as a first approach to an extensive use of the remote sensing technology for asbestos mapping over large areas and the encouraging results will support Public Administrations for decision making strategies and policies for their removal.