Hyperspectral images often have low spatial resolution due to the sensor sizes required to capture the required spectral response. Super-resolution (SR) techniques try to mitigate this by injecting more detail in the upscaled image, either with numerical methods or deep learning and Convolution Neural Networks. In the deep learning methods, the models learn image details by inferring a high-resolution (HR) image from a synthetic low-resolution (LR) image that simulates the natural degradation of sensors by applying resampling (to reduce the image detail) and noising (to add small errors and interference). Often disregarded in the literature, the resampling method applied to generate the synthetic image can impact greatly the deep learning model training. This work, evaluate several resampling techniques to measure this impact using the Harvard hyperspectral dataset. Results showed that the Lanczos filter was the best among eight other resampling methods. The Nemenyi and Friedman ranking statistical tests also indicated that the Cubic-Spline, Bicubic, and RMS achieved good results.

Improvement in spatial resolution of remote sensing images will always be a hot-topic since the sharpening of image objects is mandatory for many appications. Even thought image instruments have received great improvements in the recent years, super-resolution techniques are welcome to increase even more the level of quality of the data for further interpretations. In this paper we present a multi-frame super-resolution approach that uses a set of UAV images acquired at the same spot, but with slight different perspectives caused by random fluctuations. The small off-sets between consecutive pixels of the low spatial resolution images are considered at subpixel level to feed an arithmetic system of equations able to produce high resolution pixels, which are the unknowns of the system. The results are soundness and could show visual enhancement. However, further developments is need to in deep undertanding and possible advancement of the designed approach.

Kerogen type in source rocks is directly related to its hydrocarbon generation potential. Its determination is often carried out with destructive methods. This study presents a non-destructive technique as an alternative to determine kerogen type using hyperspectral data and machine learning techniques. To present the technique, models were training using Support Vector Machines, K Nearest Neighbors, and Random Forest classifiers on spectral data collected in rock samples acquired from Taubaté Basin, Brazil, of an outcrop with high hydrocarbon generation potential. The models were trained and evaluated using spectral signatures measured with a spectroradiometer and the results were also tested on hyperspectral images of the samples. The experiments described here achieved accuracy above 0.8 with precision and recall above 0.62 and 0.8, respectively, for every kerogen type, indicating the soundness of the classification.

The digital geoscience revolution is modifying the technologies geoscientists use to acquire and process data with the coming of digital outcrop models, hyperspectral data among many methods. These improvements in technology create new challenges in visualization, manipulation, and modeling of the data acquired, opening new research possibilities. In this paper, we present a novel system to visualize, manipulate and correlate geochemical and hyperspectral data, Digital Outcrop Models, and 3D rock samples using state-of-the-art immersive virtual reality techniques. We present a study case using a visualization and data set on an open pit quarry outcrop of a potential analog for hydrocarbon source rocks from Tremembé Formation (Taubaté Basin, Brazil).

The present study explores the capabilities of a low-cost and standard drone for the generation of a mixed dataset processed as an orthophotomosaic, and surface exposure mesh over the inaccessible wave-washed Wildlife Refuge of Ilha dos Lobos in the southern Brazilian coast. The area comprises an important Marine Protected Area used as a seasonal resting refuge of South American sea lions and fur seals that is continuously washed by wave actions, restricting human access for conventional field mapping. The results have estimated a total surface exposure of the island of 20,806.8 m2, which is an increase of 14.01 % on the total area relative to the most exposed single survey done so far. In futures works, these results will provide data for the geological characterization of the island, which will increase the information on the environment given support to the Wildlife Refuge of Ilha dos Lobos management plan.