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iSAM - towards a cost-efficient and unobtrusive experimental setup for situational awareness measurement in administrative crisis management exercises

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i-com 2025年第1期24卷 27-42页

作者： Hellmund, Tobias Kayser, Henrik Moßgraber, Jürgen Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Karlsruhe Germany

Crisis management exercises provide a unique opportunity to study situational awareness in real-time scenarios. However, the federated structure of large-scale crisis management, involving multiple decentralized teams, imposes additional challenges for researchers aiming to capture comprehensive situational awareness data. Moreover, since the primary goal of these exercises is operational readiness rather than academic research, any experimental setup must be non-intrusive and place minimal extra-demand on participants. In a future exercise, we will be allowed to measure the situational awareness of crisis management staff members to evaluate a newly developed crisis management dashboard. To do so, we need to take both theoretical, as well as practical constraints into account, as we may participate in the exercise, yet must not disturb the exercise itself. To this date, neither methodology nor software tools are available to measure the situational awareness of spatially distributed crisis managers. In this paper we introduce such methodology, which requires a balancing act between scientific and practical requirements, where some scientific standards are contradicting practical circumstances and a reasonable experimental setup must be created. To achieve this, we present a methodology supported by software that allows situational awareness assessments. Given that crisis managers operate spatially distributed during a large-scale disaster or exercise and that we lack the resources to be physically present at all locations, our introduced software aims to conduct the experiment without a researcher in every situation room. This approach facilitates comprehensive data collection and analysis without setting up a complex research environment in every situation room, thereby allowing us to meet both scientific and operational objectives effectively. © 2025 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Computer software selection and evaluation

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Semantic Neural Radiance Fields for Multi-Date Satellite Data

Semantic Neural Radiance Fields for Multi-Date Satellite Dat...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2025

作者： Wagner, Valentin Bullinger, Sebastian Bodensteiner, Christoph Arens, Michael Fraunhofer Institute of Optronics System Technologies and Image Exploitation Germany

ISBN: (纸本)9798331536626

In this work we propose a satellite specific Neural Radiance Fields (NeRF) model capable to obtain a three-dimensional semantic representation (neural semantic field) of the scene. The model derives the output from a set of multi-date satellite images with corresponding pixel-wise semantic labels. We demonstrate the robustness of our approach and its capability to improve noisy input labels. We enhance the color prediction by utilizing the semantic information to address temporal image inconsistencies caused by non-stationary categories such as vehicles. To facilitate further research in this domain, we present a dataset comprising manually generated labels for popular multi-view satellite images. Our code and dataset are available at https://***/wagnva/semantic-nerf-for-satellite-data. © 2025 IEEE.

关键词： image enhancement

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Utilizing Synthetic Data for Object Segmentation on Autonomous Heavy Machinery in Dynamic Unstructured Environments

Utilizing Synthetic Data for Object Segmentation on Autonomo...

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Autonomous systems for Security and Defence 2024

作者： Granero, Miguel Hagmanns, Raphael Petereit, Janko Fraunhofer Institute of Optronics System Technologies and Image Exploitation Karlsruhe Germany

ISBN: (纸本)9781510681224

Traditional deep learning datasets often lack representations of unstructured environments, making it difficult to acquire the ground truth data needed to train models. We therefore present a novel approach that relies on platform-specific synthetic training data. To this end, we use an excavator simulation based on the Unreal Engine to accelerate data generation for object segmentation tasks in unstructured environments. We focus on barrels, which serve as a typical example of deformable objects with different styles and shapes, which are commonly encountered in hazardous environments. Through extensive experimentation with different SOTA models for semantic segmentation, we demonstrate the effectiveness of our approach in overcoming the limitations of small training sets and show how photorealistic synthetic data substantially improves model performance, even on corner cases such as occluded or deformed objects and different lighting conditions, which is crucial to assure the robustness in real-world applications. In addition, we demonstrate the usefulness of this approach with a real-world instance segmentation application together with a ROS-based barrel grasping pipeline for our excavator platform. © 2024 SPIE.

关键词： Excavators

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Monitoring Process Phase Sequences and Durations in Industrial Batch Processes by a Hybrid Unsupervised Learning Strategy

Monitoring Process Phase Sequences and Durations in Industri...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Frey, Christian W. Fraunhofer Institute of Optronics System Technologies and Image Exploitation Iosb Karlsruhe Germany

ISBN: (纸本)9798350361025

Industrial batch processes, such as those in the pharmaceutical industry, are characterized by complex system behaviors, due to the involvement of several chemical reactions in various time critical process phases. Monitoring such processes involves several critical aspects: identifying unknown process phases, tracking their sequence and duration, and detecting anomalies within these phases. As demonstrated in several industrial applications, the ability of Self-Organizing Maps (SOMs) to detect anomalies, identify and visualize process phases is highly beneficial for comprehensive monitoring and understanding of technical processes. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of SOMs, especially in scenarios with unbalanced datasets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. The capabilities of the HULS concept compared to the standard SOM model for the detection of unknown process phases and monitoring process phase sequences and durations are evaluated using a exemplary laboratory batch process. © 2024 IEEE.

关键词： Unsupervised learning

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Spectral analysis of low temperature thermal emitters for spectrally accurate IR scene projection 21

Spectral analysis of low temperature thermal emitters for sp...

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Electro-Optical and Infrared systems: Technology and Applications XXI 2024

作者： Wiehn, Marten Wolmeringer, Miriam Henrichsen, Michael Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Gutleuthausstrasse 1 Ettlingen Germany

ISBN: (纸本)9781510681088

Multispectral IR imaging sensors provide an additional dimension of data compared to conventional devices. Testing and validating these detectors require a spectrally controlled and reproducible test scene. IR scene simulators aim to generate a realistic IR scene using artificial IR emitters and optical components. To ensure both spatial and spectral accuracy of the scene, it is crucial to have precise knowledge of the spectral characteristics of these components. This work focuses on the investigation of low-temperature thermal emitter spectra. We utilize a grating spectrometer in combination with quantum IR detectors to determine the emission spectra of thermal IR sources. The entire system is calibrated to accurately record the spectra of arbitrary thermal IR emitters. In summary, this study provides the basis for a physically accurate IR scene simulation setup, with particular emphasis on spectral realism. © 2024 SPIE.

关键词： Infrared imaging

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Temporary tank emplacements in SAR images of New Space satellites: A signature analysis 10

Temporary tank emplacements in SAR images of New Space satel...

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Target and Background Signatures X: Traditional Methods and Artificial Intelligence 2024

作者： Kuny, Silvia Hammer, Horst Thiele, Antje Schulz, Karsten Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Gutleuthausstrasse 1 Ettlingen76275 Germany

ISBN: (纸本)9781510681064

New Space synthetic aperture radar (SAR) satellites yield an increasing potential for security and defense purposes, however, as of yet the lack of insight regarding sensor specific signature properties impedes this task. SAR simulation can provide a valuable insight into these signature properties. A selection of objects relevant for reconnaissance are explored regarding their signature characteristics in SAR images of New Space satellites, using the example of the Capella Space constellation. The focus is on temporary tank emplacements, their occupancy status and visibility. With the aid of Open Source information, 3d models of temporary tank emplacements were generated for different states of occupancy. Using the CohRaS™SAR simulator of fraunhofer IOSB these models were then simulated with sensor and acquisition parameters matching those of Capella Space. system specific properties, such as the variation of system noise levels or the limits of acquisition geometry are taken into account. Finally, a comparison between simulated signatures and signatures of real SAR imagery, as well as a discussion about the qualitative properties of these signatures are provided. © 2024 SPIE.

关键词： Tanks (containers)

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SAR speckle filtering with CNN using simulated training data 3

SAR speckle filtering with CNN using simulated training data

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Microwave Remote Sensing: Data Processing and Applications III 2024

作者： Hammer, Horst Kuny, Silvia Thiele, Antje Fraunhofer Institute of Optronics System Technologies and Image Exploitation IOSB Gutleuthausstrasse 1 Ettlingen76275 Germany

ISBN: (纸本)9781510680982

SAR sensors play an important role for a wide range of remote sensing applications due to their all-weather capabilities and their independence from sunlight. However, SAR images are more difficult to interpret than electro-optical images, and are affected by speckle noise, a multiplicative phenomenon that is created by the coherent nature of the image formation process and that might severely hinder image interpretation. Thus, speckle filtering is of great importance and many different approaches have been suggested. While most classical speckle filters use the local statistics in a sliding window, more recently non-local filtering and Convolutional Neural Networks (CNNs) have been used. The approach suggested in this paper falls into the latter category. For supervised CNN approaches, many training chips are necessary to teach the network the task it should solve. In the case of speckle filtering, many speckled/unspeckled image pairs are necessary. In this paper, we suggest creating training data for a U-Net architecture using SAR simulation. The simulation results of several large 3d scenes are shown, which in turn are used for the training of the U-Net for speckle filtering. First results on a TerraSAR-X image are shown and the potential and limitations of the current state of the approach are discussed. © 2024 SPIE.

关键词： Radar imaging

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A Hybrid Unsupervised Learning Strategy for Monitoring Complex Industrial Manufacturing Processes 49

A Hybrid Unsupervised Learning Strategy for Monitoring Compl...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Frey, Christian W. Fraunhofer Institute of Optronics System Technologies and Image Exploitation Iosb Karlsruhe Germany

ISBN: (纸本)9798350331820

Industrial production processes are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. Anomaly detection is a crucial component of these monitoring systems, with machine learning (ML) methods offering significant advantages over traditional statistical techniques. Clustering-based unsupervised anomaly detection algorithms, such as Self-Organizing Maps (SOMS), are particularly valuable in process monitoring, as they can detect anomalies and identify process phases without requiring labeled training data. However, unsupervised learning methods can be sensitive to the inherent properties of the training data, particularly in cases of strong correlation between features or unbalanced datasets. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial production processes. The proposed method combines existing techniques to address the challenges posed by correlated and unbalanced data. Experimental results from a synthetic dataset demonstrate the effectiveness of the developed hybrid approach. © 2023 IEEE.

关键词： Self organizing maps

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Autonomous Sensor Control for Mobile Platforms Operating in Teams

Autonomous Sensor Control for Mobile Platforms Operating in ...

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Autonomous systems for Security and Defence 2024

作者： Tchouchenkov, I. Segor, F. Kollmann, M. Buller, A. Fraunhofer Institute for Optronics System Technologies and Image Exploitation IOSB Fraunhoferstr. 1 Karlsruhe76131 Germany

ISBN: (纸本)9781510681224

New algorithms for appropriate planning and dynamic control of pan-tilt-zoom sensors (stationary or mounted on mobile systems) in in context of reconnaissance missions have been developed at fraunhofer IOSB. These algorithms are based on the prior proposed solutions for an efficient control of robotic vehicles and groups of heterogeneous robotic vehicles. In this paper two specific algorithms (deterministic and non-deterministic) are referenced, and it is exploited how these algorithms - originally developed for the control of unmanned vehicles of potential different domains - can be adapted to also support the intelligent autonomous sensor control. The aim is to maximize the effectiveness of these sensors when used in reconnaissance missions. The deterministic algorithm is based on extensive pre-planning that considers all relevant aspects of the task at hand and the optical sensors to be used like target area, restricted zones, fields of view, resolutions, zoom, and uses approximations and assumptions to determine the best possible area coverage. The non-deterministic algorithm does not undertake pre-planning but rather provides basic behaviors and mission relevant compiled information that is used by the autonomous control system to identify the most reasonable actions based on the current situation. Both algorithm types are suitable for the autonomous control of (heterogeneous) cooperative sensors without any operator interaction. To provide effective and efficient reconnaissance, the usage of each sensor assigned to the operation must be optimized and depending on the task ensure best possible coverage of the mission-relevant area, focus on certain areas by increasing the scanning cadence, etc. To provide sufficient image resolution and quality, the footprint should cover each specified target for a defined time period with a suitable zoom level. Changing alignment angles and relative positions must be continuously taken into account. Therefore, regarding senso

关键词： image resolution

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Symbol error probability for coherent laser communications systems in atmospheric turbulence: Gaussian beam analysis 7

Symbol error probability for coherent laser communications s...

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Environmental Effects on Light Propagation and Adaptive systems VII 2024

作者： Toselli, Italo Gladysz, Szymon Fraunhofer IOSB Ettlingen Fraunhofer Institute of Optronics System Technologies and Image Exploitation Gutleuthausstr. 1 Ettlingen76275 Germany

ISBN: (纸本)9781510680968

In this paper, we investigate how atmospheric turbulence impacts Symbol Error Probability (SEP) of free-space optical communication systems based on the phase-shift keying heterodyne coherent receiver scheme. The derivation assumes the validity of the Maréchal approximation and requires a high number of "coherence cells" at the receiver pupil (ergodic hypothesis). Results show that such conditions can both be satisfied at the same time when tip and tilt correction is applied. We also found that better performances in terms of SEP are possible when the receiver is located in the far field (supposing a collimated beam at the transmitter). Our analysis is valid for Gaussian beams affected by Kolmogorov turbulence. © 2024 SPIE.

关键词： Gaussian beams

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