The Carajás mineral province encompasses the world's largest iron reserves with excavation carried out through open pit benching. Mining operations in the area have significant areas of rock mass movements and surface displacements that potentially lead to slope instabilities with risks to personnel, equipment and production. Instabilities can be expected due to deep excavations in rock masses of low geomechanical quality, blasting practices and heavy precipitation. In this paper, ground instabilities are monitored through an integrated SAR analysis based on a data-stack of 33 TerraSAR-X images. This approach was designed to monitor distinct displacement regimes, ranging from small to high deformation rates, and to map surface changes, based on variations of radar reflectivity. Results were compared with field information (total station/prisms, ground based radar, geological and geomechanical maps), and the approach showed to be effective for monitoring ground deformation in the region. Due to its capabilities (synoptic view, high accuracy, dense grid sampling), the complementary use of space-based SAR with field monitoring systems proved to be strategic for operational mining planning and risk assessment in this challenging environment

Sampling the Earth’s surface using airborne LASER scanning (ALS) systems suffers from several factors inherent to the LASER system itself as well as external factors, such as the presence of particles in the atmosphere, and/or multi-path returns due to reflections. The resulting point cloud may therefore contain some outliers and removing them is an important (and difficult) step for all subsequent processes that use this kind of data as input. In the literature, there are several approaches for outlier removal, some of which require external information, such as spatial frequency characteristics or presume parametric mathematical models for surface fitting. A limitation on the height histogram filtering approach was identified from the literature review: outliers that lie within the ground elevation difference might not be detected. To overcome such a limitation, this paper proposes an adaptive alternative based on point cloud cell subdivision. Instead of computing a single histogram for the whole dataset, the method applies the filtering to smaller patches, in which the ground elevation difference can be ignored. A study area was filtered, and the results were compared quantitatively with two other methods implemented in both free and commercial software packages. The reference data was generated manually in order to provide useful quality measures. The experiment shows that none of the tested filters was able to reach a level of complete automation, therefore manual corrections by the operator are still necessary.

Image acquisition systems based on multi-head arrangement of digital cameras are attractive alternatives enabling a larger imaging area when compared to a single frame camera. The calibration of this kind of system can be performed in several steps or by using simultaneous bundle adjustment with relative orientation stability constraints. The paper will address the details of the steps of the proposed approach for system calibration, image rectification, registration and fusion. Experiments with terrestrial and aerial images acquired with two Fuji FinePix S3Pro cameras were performed. The experiments focused on the assessment of the results of self-calibrating bundle adjustment with and without relative orientation constraints and the effects to the registration and fusion when generating virtual images. The experiments have shown that the images can be accurately rectified and registered with the proposed approach, achieving residuals smaller than one pixel.

Aerial images of urban areas have been used as base information for a diversity of applications. Considering the great quantity of tall buildings in these areas, it is important to have a method to automatically generate a product called true orthophoto mosaic, which represents all objects above the ground (buildings, bridges, etc.) in their true location. However, to create a true orthophoto, it is necessary to consider the occlusions caused by the surface height variation and to compensate for the lack of information using adjacent aerial images. The automatic occlusion detection is the bottleneck during the true orthophoto mosaic generation. The main aim of this paper is to introduce a new approach for occlusion detection - the surface-gradient-based method (SGBM) applied to a triangulated irregular network (TIN) representation. The originality of the SGBM is the occlusion detection principle, which is based on the concept of surface gradient behavior analysis over a TIN surface. The current methods interpolate a point cloud into a gridded digital surface model, which can introduce artifacts to the representation. The SGBM represents the surface as a TIN-based solid by taking into account the Delaunay constraint in the original point cloud, avoiding the interpolation step. The occlusions are then compensated using specific cost functions and refined via color blending. Experiments were performed and the results were assessed by using quality indicators (completeness), the consistency of orthoimage mosaic, and the time of processing. Experimental results demonstrated the feasibility of the SGBM for occlusion detection in the true orthophoto generation.

The development of unmanned aerial vehicles (UAVs) along with that of positioning and imaging sensors has promoted the use of photogrammetric techniques for applications other than the conventional acquisition of the Earth’s surface information. Moreover, the accuracy of photogrammetric products depends on several parameters, such as sensor quality and stability, image scale, strip overlap, and the number and distribution of ground control points (GCPs). Although technological developments have improved the automation and accuracy of the photogrammetric process, the acquisition of GCPs remains a highly time-consuming task. To address this problem, this study investigated the positional error of digital surface models (DSMs) generated via the post-processing of data acquired with a SenseFly eBee Classic, with respect to the number and distribution of GCPs. A single flight was conducted over a test field located at an urban expansion region of Presidente Prudente, State of São Paulo, Brazil. Nine DSMs were generated using different configurations of the number and distribution of GCPs. An accuracy assessment was performed based on 20 horizontal check points and 270 vertical check points determined by global navigation satellite system (GNSS) positioning. The vertical error surfaces were generated by Kriging. The Student’s t-test, Chi-square test, and root mean square error (RMSE) were used for the positional accuracy assessment. The results show that the qualities of the products generated by GNSS-supported Aerial Triangulation (GNSS-AT) without GCPs were in accordance with those indicated by the UAV manufacturer. In contrast, the results obtained by considering the GNSS-AT with different GCPs were not in accordance with the manufacturer’s claims. Finally, RMSE values of approximately 3 ground sample distance (GSD) for the horizontal component and 5 GSD for the vertical component are achievable with at least three well-distributed GCPs; however, the use of additional points is encouraged.