Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport–Beach–Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

Re-engineering and reuse of programs implemented on programmable logic controllers (PLCs) became vital in the recent years. This is because the programming of PLC programs is an exhaustive and expensive activity. This paper outlines a re-engineering approach based on the formalization of PLC programs. The approach utilizes XML as an intermediate step for the transformation of the existing PLC programs into a vendor independent format. After this transformation XML is used as a basis for the visualization and formalization of the PLC code. The formalization consists of two main steps: first the modular structure of the program is transferred to an UML class diagram and in the second step the algorithms contained in the modules are converted to state diagrams. This conversion uses an abstraction method to avoid large automata

This research paper presents a novel approach for occlusion inpainting in thermal images to efficiently segment and enhance obscured regions within these images. The increasing reliance on thermal imaging in fields like surveillance, security, and defense necessitates the accurate detection of obscurants such as smoke and fog. Traditional methods often struggle with these complexities, leading to the need for more advanced solutions. Our proposed methodology uses a Generative Adversarial Network (GAN) to fill occluded areas in thermal images. This process begins with an obscured region segmentation, followed by a GAN-based pixel replacement in these areas. The methodology encompasses building, training, evaluating, and optimizing the model to ensure swift real-time performance. One of the key challenges in thermal imaging is identifying effective strategies to mitigate critical information loss due to atmospheric interference. Our approach addresses this by employing sophisticated deep-learning techniques. These techniques segment, classify and inpaint these obscured regions in a patch-wise manner, allowing for more precise and accurate image restoration. We propose utilizing architectures similar to Pix2Pix and UNet networks for generative and segmentation tasks. These networks are known for their effectiveness in image-to-image translation and segmentation tasks. Our method enhances the segmentation and inpainting process by leveraging their architectural similarities. To validate our approach, we provide a quantitative analysis and performance comparison. We include a quantitative comparison between (Pix2Pix and UNet) and our combined architecture. The comparison focuses on how well each model performs in terms of accuracy and speed, highlighting the advantages of our integrated approach. This research contributes to advancing thermal imaging techniques, offering a more robust solution for dealing with obscured regions. The integration of advanced deep learning models holds the potential to significantly improve image analysis in critical applications like surveillance and security.

This publication presents the results of a case study carried out in Pakistan focused on local water governance systems in Chitral and the local capacity to adapt to changing climate conditions as part of a larger project documenting local adaptation strategies to climate-induced water stress and hazards in the HKH region. The publication looks at the impact of governmental and non-governmental water sector interventions on the functioning of traditional institutions and how such interventions affect the adaptive capacity of local communities.