Fakultät für Bauingenieurwesen und GeodäsieFrei zugängliche Publikationen aus der Fakultät für Bauingenieurwesen und Geodäsiehttps://www.repo.uni-hannover.de/handle/123456789/32024-03-19T05:37:56Z2024-03-19T05:37:56ZThermal anomaly detection based on saliency analysis from multimodal imaging sourcesSledz, A.Heipke, C.https://www.repo.uni-hannover.de/handle/123456789/167572024-03-19T02:00:06Z2021-01-01T00:00:00ZThermal anomaly detection based on saliency analysis from multimodal imaging sources
Sledz, A.; Heipke, C.
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.
2021-01-01T00:00:00ZAdversarial discriminative domain adaptation for deforestation detectionNoa, J.Soto, P.J.Costa, G.A.O.P.Wittich, D.Feitosa, R.Q.Rottensteiner, F.https://www.repo.uni-hannover.de/handle/123456789/167582024-03-19T02:00:07Z2021-01-01T00:00:00ZAdversarial discriminative domain adaptation for deforestation detection
Noa, J.; Soto, P.J.; Costa, G.A.O.P.; Wittich, D.; Feitosa, R.Q.; Rottensteiner, F.
Although very efficient in a number of application fields, deep learning based models are known to demand large amounts of labeled data for training. Particularly for remote sensing applications, responding to that demand is generally expensive and time consuming. Moreover, supervised training methods tend to perform poorly when they are tested with a set of samples that does not match the general characteristics of the training set. Domain adaptation methods can be used to mitigate those problems, especially in applications where labeled data is only available for a particular region or epoch, i.e., for a source domain, but not for a target domain on which the model should be tested. In this work we introduce a domain adaptation approach based on representation matching for the deforestation detection task. The approach follows the Adversarial Discriminative Domain Adaptation (ADDA) framework, and we introduce a margin-based regularization constraint in the learning process that promotes a better convergence of the model parameters during training. The approach is evaluated using three different domains, which represent sites in different forest biomes. The experimental results show that the approach is successful in the adaptation of most of the domain combination scenarios, usually with considerable gains in relation to the baselines.
2021-01-01T00:00:00ZMaximum consensus localization using lidar sensorsAxmann, J.Brenner, C.https://www.repo.uni-hannover.de/handle/123456789/167552024-03-19T02:00:05Z2021-01-01T00:00:00ZMaximum consensus localization using lidar sensors
Axmann, J.; Brenner, C.
Real world localization tasks based on LiDAR usually face a high proportion of outliers arising from erroneous measurements and changing environments. However, applications such as autonomous driving require a high integrity in all of their components, including localization. Standard localization approaches are often based on (recursive) least squares estimation, for example, using Kalman filters. Since least squares minimization shows a strong susceptibility to outliers, it is not robust. In this paper, we focus on high integrity vehicle localization and investigate a maximum consensus localization strategy. For our work, we use 2975 epochs from a Velodyne VLP-16 scanner (representing the vehicle scan data), and map data obtained using a Riegl VMX-250 mobile mapping system. We investigate the effects of varying scene geometry on the maximum consensus result by exhaustively computing the consensus values for the entire search space. We analyze the deviations in position and heading for a circular course in a downtown area by comparing the estimation results to a reference trajectory, and show the robustness of the maximum consensus localization.
2021-01-01T00:00:00ZKhalasa date palm leaf fiber as a potential reinforcement for polymeric composite materialsMahdi, ElsadigOchoa, Daniel R. HernándezVaziri, AshkanDean, AamirKucukvar, Murathttps://www.repo.uni-hannover.de/handle/123456789/167622024-03-19T02:00:08Z2020-01-01T00:00:00ZKhalasa date palm leaf fiber as a potential reinforcement for polymeric composite materials
Mahdi, Elsadig; Ochoa, Daniel R. Hernández; Vaziri, Ashkan; Dean, Aamir; Kucukvar, Murat
The circular economy (CE) proposes a closed-loop supply chain-based production system and reduces the ecological systems' negative impacts. CE proposes a paradigm shift from a linear economy to a circular economy with the principles of 3Rs: reduce, reuse, and recycle. CE applications can be a viable option for the sustainable production of polymeric composite materials by decreasing the cost and improving product lifetimes and mechanical performance. This paper explores Khalasa date palm leaf fiber (KDPLF) as a reinforcement for polymeric composite materials. To this end, it is essential to examine their morphology, material properties, chemical composition, and water uptake. The investigated fiber was obtained from the Qatar University farm. The morphology examination was carried out using scanning electron microscopy. Thermogravimetric analysis has been used to examine the thermal stability of KDPLF. Morphological examination indicates that the lumen size for Khalasa is 32.8 ± 15.9 µm. The SEM morphology of the KDPLF cross-section showed high hemicellulose content. Tensile properties revealed that Khalasa fiber had tensile strength/tensile modulus of 47.99 ± 13.58 MPa and 2.1 ± 0.40 GPa, respectively. The results are also demonstrated that high variation in the mechanical properties and morphology was showed in KDPLF. Water uptake has significant effects on the properties of KDPLF/epoxy composite. Accordingly, as the moisture absorption of KDPLF/epoxy increases, its strength and stiffness decrease. As the moisture absorption of KDPLF/epoxy increases, its toughness increases.
2020-01-01T00:00:00Z