<p><strong>Abstract.</strong> This paper deals with two aspects of photogrammetric processing of thermal images: image quality and 3D reconstruction quality. The first aspect of the paper relates to the influence of day light on Thermal InfraRed (TIR) images captured by an Unmanned Aerial Vehicle (UAV). Environmental factors such as ambient temperature and lack of sun light affect TIR image quality. We acquire image sequences of the same object during day and night and compare the generated orthophotos according to different metrics like contrast and signal-to-noise ratio (SNR). Our experiments show that performing TIR image acquisition during night time provides a better thermal contrast, regardless of whether we compute contrast over the whole image or over small patches. The second aspect investigated in this work is the potential of using TIR images for photogrammetric tasks such as the automatic generation of Digital Surface Models (DSM) and orthophotos. Due to the low geometrical resolution of a TIR camera and the low image quality in terms of contrast and noise compared to RGB images, the TIR DSM suffers from reconstruction errors and an orthophoto generated using the TIR DSM and TIR images is visibly influenced by those errors. We therefore include measurements of the UAVs positions during image capturing provided by a Global Navigation Satellite System (GNSS) receiver to retrieve position and orientation of TIR and RGB images in the same world coordinate system. To generate an orthophoto from TIR images, they are projected onto the DSM reconstructed from RGB images. This procedure leads to a TIR orthophoto of much higher quality in terms of geometrical correctness.</p>

Abstract. District Heating Systems (DHS) distribute heat in terms of hot water or steam. Loss of media (water or steam), and thus energy, is expensive and has a negative impact on the environment. It is therefore of great interest to develop techniques to detect and localize potential leakages fast and cost-effectively. To avoid interference with the operating process of a DHS, airborne thermography comes into place. The use of Unmanned Aerial Vehicles (UAV) as a flexible and low-cost platform equipped with a Thermal InfraRed (TIR) camera is a promising alternative to a conventional manned flight.This paper describes a method for automatic thermal anomaly detection of a DHS. Thermal data acquisition using a UAV is followed by photogrammetric processing of the TIR images. In this way, a thermal orthophoto is produced. The next step is an identification of anomalies by means of image analysis. We apply the Laplacian of Gaussian (LoG) blob detector to find high temperature regions in areas of interest of a thermal orthomosaic. This area of interest is defined around the DHS position in the images as defined in a Geographic Information System. Finally, segmentation and classification are employed to reduce false alarms and localize thermal anomalies. An experimental evaluation using real-world data is presented, showing that the developed method deliverers promising results.

Abstract. Thermal anomaly detection has an important role in remote sensing. One of the most widely used instruments for this task is a Thermal InfraRed (TIR) camera. In this work, thermal anomaly detection is formulated as a salient region detection, which is motivated by the assumption that a hot region often attracts attention of the human eye in thermal infrared images. Using TIR and optical images together, our working hypothesis is defined in the following manner: a hot region that appears as a salient region only in the TIR image and not in the optical image is a thermal anomaly. This work presents a two-step classification method for thermal anomaly detection based on an information fusion of saliency maps derived from both, TIR and optical images. Information fusion, based on the Dempster-Shafer evidence theory, is used in the first phase to find the location of regions suspected to be thermal anomalies. This classification problem is formulated as a multi-class problem and is carried out in an unsupervised manner on a pixel level. In the following phase, classification is formulated as a binary region-based problem in order to differentiate between normal temperature variations and thermal anomalies, while Random Forest (RF) is chosen as the classifier. In the seconds phase, the classification results from the previous phase are used as features along with temperature information and height details, which are obtained from a Digital Surface Model (DSM). We tested the approach using a dataset, which was collected from a UAV with TIR and optical cameras for monitoring District Heating Systems (DHS). Despite some limitations outlined in the paper, the presented innovative method to identify thermal anomalies has achieved up to 98.7 percent overall accuracy.