Although thermal imaging is a widely used technique in many applications and is under continuous development, one of its limits is the relatively coarse spatial resolution. Nevertheless, in the last years, a number of super-resolution algorithms have been developed which allow to enhance the resolution of the images. They can be divided in two main different categories: single-image or multiple image-based algorithms. In this work, a multiple image-based algorithm for the super-resolution was implemented, tested and applied to terrestrial thermal imaging with the aim to overcome the limitation of the low resolution. In particular, the method relies on the use of many images acquired from slightly different positions to obtain, thanks to the redundancy of observations, a super-resolution frame having an upsampling factor of four. Several tests were performed on synthetic datasets, and the accuracy of the obtained super-resolution images was investigated. Moreover, an original algorithm capable to identify gross errors during the image registration phase, which is one of the crucial phases, is presented and its reliability assessed. Results showed the effectiveness of the proposed method on both common visible images and thermal infrared ones, since discrepancies between reconstructed and reference values are reduced by 18 and 25% respectively, when compared with a conventional bicubic algorithm. Finally, the proposed method was tested on a case study concerning the thermal survey of the façade of a historical building in Bologna (Palazzo D'Accursio). A dataset of real thermal frames was acquired and a super-resolution image of the subject was generated through the developed algorithm. Strengths and weaknesses of the method were analysed and discussed in the paper.

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The relatively poor spatial resolution of thermal images is a limitation for many thermal remote sensing applications. A possible solution to mitigate this problem is super-resolution, which should preserve the radiometric content of the original data and should be applied to both the cases where a single image or multiple images of the target surface are available. In this perspective, we propose a new super-resolution algorithm, which can handle either single or multiple images. It is based on a total variation regularization approach and implements a fully automated choice of all the parameters, without any training dataset nor a priori information. Through simulations, the accuracy of the generated super-resolution images was assessed, in terms of both global statistical indicators and analysis of temperature errors at hot and cold spots. The algorithm was tested and applied to aerial and terrestrial thermal images. Results and comparisons with state-of-the-art methods confirmed an excellent compromise between the quality of the high-resolution images obtained and the required computational time.

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We provide a dataset of 3D coordinate time series of 37 continuous GNSS stations installed for stability monitoring purposes on onshore and offshore industrial settlements along a NW-SE-oriented and ~100-km-wide belt encompassing the eastern Italian coast and the Adriatic Sea. The dataset results from the analysis performed by using different geodetic software (Bernese, GAMIT/GLOBK and GIPSY) and consists of six raw position time series solutions, referred to IGb08 and IGS14 reference frames. Time series analyses and comparisons evidence that the different solutions are consistent between them, despite the use of different software, models, strategy processing and frame realizations. We observe that the offshore stations are subject to significant seasonal oscillations probably due to seasonal environmental loads, seasonal temperature-induced platform deformation and hydrostatic pressure variations. Many stations are characterized by non-linear time series, suggesting a complex interplay between regional (long-term tectonic stress) and local sources of deformation (e.g. reservoirs depletion, sediment compaction). Computed raw time series, logs files, phasor diagrams and time series comparison plots are distributed via PANGAEA (https://www.pangaea.de).

It has been demonstrated that precise point positioning (PPP) is a powerful tool in geodetic and geodynamic applications. As is known, it provides solutions in the reference system of the satellite orbits. We focuses on the strategy to transform PPP solutions into the International Terrestrial Reference System (ITRS) by applying a set of local Helmert transformation parameters obtained from a regional network rather than using global parameters. In order to carry out this test, a regional network composed of 14 stations was analyzed using GIPSY-OASIS II software, over a period of 6 years. Two solutions differently aligned to the ITRS were compared in terms of accuracy, scattering, frequency content and local movements. One solution is aligned to IGb08 through the X-files provided by JPL, while the other is aligned to the European reference frame densification of IGb08 using customized regional X-files. Therefore, both are updated realizations of the ITRS. The test shows that a regional, instead of a global, alignment to the ITRS can significantly improve the repeatability of the solutions. A small improvement can also be found in terms of agreement with the regional densification of IGb08. The analysis of the signal content in the differently aligned time series allowed some differences to be found, in terms of both frequency and magnitude. These differences are mainly due to an evident common signal that is defined for the whole area and which is removed when using regional alignment. Finally, residual scattering was calculated after removing the modeled signals from each time series, which results in a scatter being significantly smaller for the regional solution than for the global solution. In order to obtain these results, the choice of the reference stations is a major question and therefore discussed in detail.