Search Results (7)

Operational forest monitoring often requires fine-detail information in the form of an orthomosaic, created by stitching overlapping nadir images captured by aerial platforms such as drones. RGB drone sensors are commonly used for low-cost, high-resolution imaging that is conducive to effective orthomosaicking, but only capture visible light. Thermal sensors, on the other hand, capture long-wave infrared radiation, which is useful for early pest detection among other applications. However, these lower-resolution images suffer from reduced contrast and lack of descriptive features for successful orthomosaicking, leading to gaps or swirling artifacts in the orthomosaic. To tackle this, we propose a thermal orthomosaicking workflow that leverages simultaneously acquired RGB images. The latter are used for producing a surface mesh via structure from motion, while thermal images are only used to texture this mesh and yield a thermal orthomosaic. Prior to texturing, RGB-thermal image pairs are co-registered using an affine transformation derived from a machine learning technique. On average, the individual RGB and thermal images achieve a mutual information of 0.2787 after co-registration using our technique, compared to 0.0591 before co-registration, and 0.1934 using manual co-registration. We show that the thermal orthomosaic generated from our workflow (1) is of better quality than other existing methods, (2) is geometrically aligned with the RGB orthomosaic, (3) preserves radiometric information (i.e., surface temperatures) from the original thermal imagery, and (4) enables easy transfer of downstream tasks—such as tree crown detection from the RGB to the thermal orthomosaic. We also provide an open-source tool that implements our workflow to facilitate usage and further development. Full article

Seamline network generation is a crucial step in mosaicking multiple orthoimages. It determines the topological and mosaic contribution area for each orthoimage. Previous methods, such as Voronoi-based and AVOD (area Voronoi)-based, may generate mosaic holes in low-overlap and irregular orthoimage cases. This paper proposes a Voronoi centerline-based seamline network generation method to address this problem. The first step is to detect the edge vector of the valid orthoimage region; the second step is to construct a Voronoi triangle network using the edge vector points and extract the centerline of the network; the third step is to segment each orthoimage by the generated centerlines to construct the image effective mosaic polygon (EMP). The final segmented EMP is the mosaic contribution region. All EMPs are interconnected to form a seamline network. The main contribution of the proposed method is that it solves the mosaic holes in the Voronoi-based method when processing with low overlap, and it solves the limitation of the AVOD-based method polygon shape requirement, which can generate a complete mosaic in any overlap and any shape of the orthoimage. Five sets of experiments were conducted, and the results show that the proposed method surpasses the well-known state-of-the-art method and commercial software in terms of adaptability and effectiveness. Full article

In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method. Full article

The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics. Full article

This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as follows: flight altitudes at 30, 40, 50, 60, 70 and 80 m above ground level; two forward and side overlap settings (80%–50% and 70%–40%); and the use, or lack thereof, of ground control points. These settings were chosen to analyze their influence on the spatial quality of orthomosaicked images processed by Inpho UASMaster (Trimble, CA, USA). Changes in illumination over the study area, its impact on flight duration, and how it relates to these settings is also considered. The combined effect of these parameters on spatial quality is presented as well, defining a ratio between ground sample distance of UAV images and expected root mean square of a UAV orthomosaick. The results indicate that a balance between all the proposed parameters is useful for optimizing mission planning and image processing, altitude above ground level (AGL) being main parameter because of its influence on root mean square error (RMSE). Full article

This article describes the technical specifications and configuration of a multirotor unmanned aerial vehicle (UAV) to acquire remote images using a six-band multispectral sensor. Several flight missions were programmed as follows: three flight altitudes (60, 80 and 100 m), two flight modes (stop and cruising modes) and two ground control point (GCP) settings were considered to analyze the influence of these parameters on the spatial resolution and spectral discrimination of multispectral orthomosaicked images obtained using Pix4Dmapper. Moreover, it is also necessary to consider the area to be covered or the flight duration according to any flight mission programmed. The effect of the combination of all these parameters on the spatial resolution and spectral discrimination of the orthomosaicks is presented. Spectral discrimination has been evaluated for a specific agronomical purpose: to use the UAV remote images for the detection of bare soil and vegetation (crop and weeds) for in-season site-specific weed management. These results show that a balance between spatial resolution and spectral discrimination is needed to optimize the mission planning and image processing to achieve every agronomic objective. In this way, users do not have to sacrifice flying at low altitudes to cover the whole area of interest completely. Full article

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights. Full article