Francesco Carlo Nex

PERFORMANCE ANALYSIS OF THE SIFT OPERATOR FOR AUTOMATIC FEATURE EXTRACTION AND MATCHING IN PHOTOGRAMMETRIC APPLICATIONS

In the photogrammetry field, interest in region detectors, which are widely used in Computer Vision, is quickly increasing due to the availability of new techniques. Images acquired by Mobile Mapping Technology, Oblique Photogrammetric Cameras or Unmanned Aerial Vehicles do not observe normal acquisition conditions. Feature extraction and matching techniques, which are traditionally used in photogrammetry, are usually inefficient for these applications as they are unable to provide reliable results under extreme geometrical conditions (convergent taking geometry, strong affine transformations, etc.) and for bad-textured images. A performance analysis of the SIFT technique in aerial and close-range photogrammetric applications is presented in this paper. The goal is to establish the suitability of the SIFT technique for automatic tie point extraction and approximate DSM (Digital Surface Model) generation. First, the performances of the SIFT operator have been compared with those provided by feature extraction and matching techniques used in photogrammetry. All these techniques have been implemented by the authors and validated on aerial and terrestrial images. Moreover, an auto-adaptive version of the SIFT operator has been developed, in order to improve the performances of the SIFT detector in relation to the texture of the images. The Auto-Adaptive SIFT operator (A2 SIFT) has been validated on several aerial images, with particular attention to large scale aerial images acquired using mini-UAV systems.

Dense image matching: Comparisons and analyses

The paper presents a critical review and analysis of dense image matching algorithms. The analyzed algorithms stay in the commercial as well open-source domains. The employed datasets include scenes pictured in terrestrial and aerial blocks, acquired with convergent and parallel-axis images and different scales. Geometric analyses are reported, comparing the dense point clouds with ground truth data.

Review of automatic feature extraction from high-resolution optical sensor data for UAV-based cadastral mapping

Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible acquisition system that appears feasible for application in cadastral mapping: high-resolution imagery, acquired using UAVs, enables a new approach for defining property boundaries. However, UAV-derived data are arguably not exploited to its full potential: based on UAV data, cadastral boundaries are visually detected and manually digitized. A workflow that automatically extracts boundary features from UAV data could increase the pace of current mapping procedures. This review introduces a workflow considered applicable for automated boundary delineation from UAV data. This is done by reviewing approaches for feature extraction from various application fields and synthesizing these into a hypothetical generalized cadastral workflow. The workflow consists of preprocessing, image segmentation, line extraction, contour generation and postprocessing. The review lists example methods per workflow step—including a description, trialed implementation, and a list of case studies applying individual methods. Furthermore, accuracy assessment methods are outlined. Advantages and drawbacks of each approach are discussed in terms of their applicability on UAV data. This review can serve as a basis for future work on the implementation of most suitable methods in a UAV-based cadastral mapping workflow.

Review of the Current State of UAV Regulations

UAVs—unmanned aerial vehicles—facilitate data acquisition at temporal and spatial scales that still remain unachievable for traditional remote sensing platforms. However, current legal frameworks that regulate UAVs present significant barriers to research and development. To highlight the importance, impact, and diversity of UAV regulations, this paper provides an exploratory investigation of UAV regulations on the global scale. For this, the methodological approach consists of a research synthesis of UAV regulations, including a thorough literature review and a comparative analysis of national regulatory frameworks. Similarities and contrasting elements in the various national UAV regulations are explored including their statuses from the perspectives of past, present, and future trends. Since the early 2000s, countries have gradually established national legal frameworks. Although all UAV regulations have one common goal—minimizing the risks to other airspace users and to both people and property on the ground—the results reveal distinct variations in all the compared variables. Furthermore, besides the clear presence of legal frameworks, market forces such as industry design standards and reliable information about UAVs as public goods are expected to shape future developments.

Use of unmanned aerial vehicles in monitoring application and management of natural hazards

ABSTRACT The recent development of unmanned aerial vehicles (UAVs) has been increasing the number of technical solutions that can be used to monitor and map the effects of natural hazards. UAVs are generally cheaper and more versatile than traditional remote-sensing techniques, and they can be therefore considered as a good alternative for the acquisition of imagery and other physical parameters before, during and after a natural hazard event. This is an important added value especially for investigations over small areas (few km2). In the special issue ‘The use of Unmanned Aerial Vehicles in monitoring application and management of natural hazards’, we collected a number of case studies, aiming at providing a range of applications of monitoring and management of natural hazards assessed through the use of UAVs.

An Image-Based Approach for the Co-Registration of Multi-Temporal UAV Image Datasets

During the past years, UAVs (Unmanned Aerial Vehicles) became very popular as low-cost image acquisition platforms since they allow for high resolution and repetitive flights in a flexible way. One application is to monitor dynamic scenes. However, the fully automatic co-registration of the acquired multi-temporal data still remains an open issue. Most UAVs are not able to provide accurate direct image georeferencing and the co-registration process is mostly performed with the manual introduction of ground control points (GCPs), which is time consuming, costly and sometimes not possible at all. A new technique to automate the co-registration of multi-temporal high resolution image blocks without the use of GCPs is investigated in this paper. The image orientation is initially performed on a reference epoch and the registration of the following datasets is achieved including some anchor images from the reference data. The interior and exterior orientation parameters of the anchor images are then fixed in order to constrain the Bundle Block Adjustment of the slave epoch to be aligned with the reference one. The study involved the use of two different datasets acquired over a construction site and a post-earthquake damaged area. Different tests have been performed to assess the registration procedure using both a manual and an automatic approach for the selection of anchor images. The tests have shown that the procedure provides results comparable to the traditional GCP-based strategy and both the manual and automatic selection of the anchor images can provide reliable results.

Using UAVs for map creation and updating. A case study in Rwanda

Aerial or satellite images are conventionally used for geospatial data collection. However, unmanned aerial vehicles (UAVs) are emerging as a suitable technology for providing very high spatial and temporal resolution data at a low cost. This paper aims to show the potential of using UAVs for map creation and updating. The whole workflow is introduced in the paper, using a case study in Rwanda, where 954 images were collected with a DJI Phantom 2 Vision Plus quadcopter. An orthophoto covering 0.095 km2 with a spatial resolution of 3.3 cm was produced and used to extract features with a sub-decimetre accuracy. Quantitative and qualitative control of the UAV data products were performed, indicating that the obtained accuracies comply to international standards. Moreover, possible problems and further perspectives were also discussed. The results demonstrate that UAVs provide promising opportunities to create high-resolution and highly accurate orthophotos, thus facilitating map creation and updating.

GIMPhI: A novel vision-based navigation approach for low cost MMS

Over the last two years, the Geomatics research group at the Politecnico di Torino has developed a Low Cost System, in which only low cost sensors are involved. The system is equipped with webcams, an MEMS IMU and up to four GNSS receivers. During this development, several (non negligible) problems have been solved in order to obtain good quality after the data processing. One of the main problems of the low cost systems concerns the occurrence of GNSS outages. In this case, the IMU can only estimate the trajectory and the attitude of the vehicle for short periods. For this reason, considering the high number of frames available (about 3–5 frame per second, fps), a vision-based navigation (VBN) approach, called GIMPhI (GNSS IMU and PHotogrammetry Integration), has been adopted and tested. The navigation solutions have been refined by means of integration with a photogrammetric approach (bundle block adjustment) and a rigorous weight matrix has been adopted in order to consider the different accuracies of the various sensor observations (GNSS, IMU and images). A detailed description of this integrated approach is presented in this paper. The first tests and the achieved results are then shown in order to evaluate the reliability of the proposed approach.

Integration of airborne laser scanner and multi-image techniques for map production

In this paper, a new integrated approach between airborne laser scanner and photogrammetric aerial images is proposed. This procedure is focused on the possibility of overcoming the problems of each technique separately through their integration during the data processing. The LIDAR and multi-image matching techniques combine data in order to extract building boundaries in the space and define other map details visible from the images in an automatic way. This process could allow the extracted edges to be exploited as building boundaries in the segmentation when an ambiguity occurs in this process. The detailed description of this approach and its first promising results on an urban area will be presented and discussed.

Improving FOSS photogrammetric workflows for processing large image datasets

BackgroundIn the last decade Photogrammetry has shown to be a valid alternative to LiDAR techniques for the generation of dense point clouds in many applications. However, dealing with large image sets is computationally demanding. It requires high performance hardware and often long processing times that makes the photogrammetric point cloud generation not suitable for mapping purposes at regional and national scale. These limitations are partially overcome by commercial solutions, thanks to the use of expensive and dedicated hardware. Nonetheless, a Free and Open-Source Software (FOSS) photogrammetric solution able to cope with these limitations is still missing.MethodsIn this paper, the bottlenecks of the basic components of photogrammetric workflows -tie-points extraction, bundle block adjustment (BBA) and dense image matching- are tackled implementing FOSS solutions. We present distributed computing algorithms for the tie-points extraction and for the dense image matching. Moreover, we present two algorithms for decreasing the memory needs of the BBA. The various algorithms are deployed on different hardware systems including a computer cluster.Results and conclusionsThe usage of the algorithms presented allows to process large image sets reducing the computational time. This is demonstrated using two different datasets.