A holistic image segmentation framework for cloud detection and extraction

Shen, Dan; Xu, Haotian; Blasch, Erik; Horvath, Gregory; Pham, Khanh; Zheng, Yefeng; Ling, Haibin; Chen, Genshe

doi:10.1117/12.2015175

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…As stated in the introduction, to incresae safety and determine the value of DDDAS of information fusion concepts, weather modeling is important for safe flight operations. One example is detection and visualization of atmospheric clouds [60]. Figure 13 shows the case of abstracted weather challenges.…”

Section: Figure 11 Jview Docking Frames User Interfacementioning

confidence: 98%

Enhanced air operations using JView for an air-ground fused situation awareness udop

Blasch

2013

2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC)

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Successful management of air assets coordinates many different types of information sources from the air and on the ground for air transportation safety. An example of a combined air-ground operation is air traffic control (ATC), which requires effective visualization of information for users. In this paper, we demonstrate the use of different visualization techniques to support air-ground coordination to enhance situation awareness. Situation awareness is afforded through strategic collection, fusion and presentation of air, ground, and space data. Visual analytics examples are presented using the Java Viewer (JView) User Defined Operating Picture (UDOP) that utilizes the Airspace Concepts Evaluation System (ACES) for civilian aviation applications.

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Section: Figure 11 Jview Docking Frames User Interfacementioning

confidence: 98%

Enhanced air operations using JView for an air-ground fused situation awareness udop

Blasch

2013

2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC)

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“…Another definition of a domain includes the environment scenario domain where the operating conditions (sensor target, environment) [41] change between training, testing, and deployment. When the source data does not cover all the exemplars of the environment in training, but are exposed to different environmental conditions (such as lighting) when deployment, there are domain contextualization (such as for image segmentation [42]). For many unique scenarios such as humanitarian support to unknown natural disasters [43], then the operating scenario would be the target domain for which there is no source domain information.…”

Section: Multidomain Operations With Heterogeneous Datamentioning

confidence: 99%

Human-machine cooperative AI decision-making with heterogeneous data

Blasch,

Bastian,

Aved

et al. 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

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Many techniques have been developed for sensor and information fusion, machine and deep learning, as well as data and machine analytics. Currently, many groups are exploring methods for human-machine teaming using saliency and heat maps, explainable and interpretable artificial intelligence, as well as user-defined interfaces. However, there is still a need for standard metrics for test and evaluation of systems utilizing artificial intelligence (AI), such as deep learning (DL), to support the AI principles. In this paper, we explore the elements associated with the opportunities and challenges emerging from designing, testing, and evaluating such future systems. The paper highlights the MAST (multi-attribute scorecard table), and more specifically the MAST criteria ―analysis of alternatives‖ by measuring the risk associated with an evidential DL-based decision. The concept of risk includes the probability of a decision as well as the severity of the choice, from which there is also a need for an uncertainty bound on the decision choice which the paper postulates a risk bound. Notional analysis for a cyber networked system is presented to guide to interactive process for test and evaluation to support the certification of AI systems as to the decision risk for a human-machine system that includes analysis from both the DL method and a user.

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Joint Manifold Learning Based Distributed Sensor Fusion of Image and Radio-Frequency Data

Shen

Zulch

et al. 2019

2019 IEEE Aerospace Conference

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A holistic image segmentation framework for cloud detection and extraction

Cited by 6 publications

References 19 publications

Enhanced air operations using JView for an air-ground fused situation awareness udop

Enhanced air operations using JView for an air-ground fused situation awareness udop

Human-machine cooperative AI decision-making with heterogeneous data

Joint Manifold Learning Based Distributed Sensor Fusion of Image and Radio-Frequency Data

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