Abstract. Automatic feature matching is a crucial step in Structure-from-Motion (SfM) applications for 3D reconstruction purposes. From an historical perspective we can say now that SIFT was the enabling technology that made SfM a successful and fully automated pipeline. SIFT was the ancestor of a wealth of detector/descriptor methods that are now available. Various research activities have tried to benchmark detector/descriptors operators, but a clear outcome is difficult to be drawn. This paper presents an ISPRS Scientific Initiative aimed at providing the community with an educational open-source tool (called PhotoMatch) for tie point extractions and image matching. Several enhancement and decolorization methods can be initially applied to an image dataset in order to improve the successive feature extraction steps. Then different detector/descriptor combinations are possible, coupled with different matching strategies and quality control metrics. Examples and results show the implemented functionality of PhotoMatch which has also a tutorial for shortly explaining the implemented methods.

Global awareness of environmental issues has boosted interest in renewable energy resources, among which solar energy is one of the most attractive renewable sources. The massive growth of PV plants, both in number and size, has motivated the development of new approaches for their inspection and monitoring. In this paper, a rigorous drone photogrammetry approach using optical Red, Green and Blue (RGB) and Infrared Thermography (IRT) images is applied to detect one of the most common faults (hot spots) in photovoltaic (PV) plants. The latest advances in photogrammetry and computer vision (i.e., Structure from Motion (SfM) and multiview stereo (MVS)), together with advanced and robust analysis of IRT images, are the main elements of the proposed methodology. We developed an in-house software application, SunMap, that allows automatic, accurate, and reliable detection of hot spots on PV panels. Along with the identification and geolocation of malfunctioning PV panels, SunMap provides high-quality cartographic products by means of 3D models and true orthophotos that provide additional support for maintenance operations. Validation of SunMap was performed in two different PV plants located in Spain, generating positive results in the detection and geolocation of anomalies with an error incidence lower than 15% as validated by the manufacturer’s standard electrical tests.

The accurate and reliable extraction and matching of distinctive features (keypoints) in multi-view and multi-modal datasets is still an open research topic in the photogrammetric and computer vision communities. However, one of the main milestones is selecting which method is a suitable choice for specific applications. This encourages us to develop an educational tool that encloses different hand-crafted and learning-based feature-extraction methods. This article presents PhotoMatch, a didactical, open-source tool for multi-view and multi-modal feature-based image matching. The software includes a wide range of state-of-the-art methodologies for preprocessing, feature extraction and matching, including deep learning detectors and descriptors. It also provides tools for a detailed assessment and comparison of the different approaches, allowing the user to select the best combination of methods for each specific multi-view and multi-modal dataset. The first version of the tool was awarded by the ISPRS (ISPRS Scientific Initiatives, 2019). A set of thirteen case studies, including six multi-view and six multi-modal image datasets, is processed by following different methodologies, and the results provided by the software are analysed to show the capabilities of the tool. The PhotoMatch Installer and the source code are freely available.