Application of VNIIRS for target tracking

Blasch, Erik; Kahler, Bart

doi:10.1117/12.2177543

Cited by 7 publications

(5 citation statements)

References 79 publications

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“…45,8,46 Additional investigations have shown relationships between loss in image interpretability and objective image metrics. 35 The approach is to compare the NIIRS loss as rated by expert human observers to predict NIIRS loss reported by CoDIFI. The imagery data used for validation are new images that were not used in the development of CoDIFI.…”

Section: Validation Of the Codifi Methodsmentioning

confidence: 99%

“…28,29 Many examples to compute the NIIRS have been reported 11 and updates are included in the Motion Imagery Standards Board. Recent efforts include the Video-National Imagery Interpretability Rating Scale (VNIIRS) [30][31][32][33] which can be used for video analysis, 34,35 but they still require extensive validation of how to be applied in dynamic imagery collections.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of compression-induced image interpretability degradation

et al. 2018

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Image compression is an important component in modern imaging systems as the volume of the raw data collected is increasing. To reduce the volume of data while collecting imagery useful for analysis, choosing the appropriate image compression method is desired. Lossless compression is able to preserve all the information, but it has limited reduction power. On the other hand, lossy compression, which may result in very high compression ratios, suffers from information loss. We model the compression-induced information loss in terms of the National Imagery Interpretability Rating Scale or NIIRS. NIIRS is a user-based quantification of image interpretability widely adopted by the Geographic Information System community. Specifically, we present the Compression Degradation Image Function Index (CoDIFI) framework that predicts the NIIRS degradation (i.e., a decrease of NIIRS level) for a given compression setting. The CoDIFI-NIIRS framework enables a user to broker the maximum compression setting while maintaining a specified NIIRS rating.

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Section: Validation Of the Codifi Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of compression-induced image interpretability degradation

et al. 2018

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“…One example is image quality [99], where high-quality imagery leads to better multi-INT fusion products [100]. Both subjective and objective measures [101] have been developed to measure imagery intelligence between the user and the machine [102] with recent efforts for using the static image National Imagery Interpretability Rating Scale (NIIRS) and dynamic videos (VNIIRS) [103]. Thus, there is a need to look at other human factor rating techniques and adapt them for other multi-INT processes.…”

Section: Physical: Timely Data Quality Perception and Manipulationmentioning

confidence: 99%

Multi-intelligence critical rating assessment of fusion techniques (MiCRAFT)

Blasch

2015

SPIE Proceedings

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Assessment of multi-intelligence fusion techniques includes credibility of algorithm performance, quality of results against mission needs, and usability in a work-domain context. Situation awareness (SAW) brings together low-level information fusion (tracking and identification), high-level information fusion (threat and scenario-based assessment), and information fusion level 5 user refinement (physical, cognitive, and information tasks). To measure SAW, we discuss the SAGAT (Situational Awareness Global Assessment Technique) technique for a multi-intelligence fusion (MIF) system assessment that focuses on the advantages of MIF against single intelligence sources. Building on the NASA TLX (Task Load Index), SAGAT probes, SART (Situational Awareness Rating Technique) questionnaires, and CDM (Critical Decision Method) decision points; we highlight these tools for use in a Multi-Intelligence Critical Rating Assessment of Fusion Techniques (MiCRAFT). The focus is to measure user refinement of a situation over the information fusion quality of service (QoS) metrics: timeliness, accuracy, confidence, workload (cost), and attention (throughput). A key component of any user analysis includes correlation, association, and summarization of data; so we also seek measures of product quality and QuEST of information. Building a notion of product quality from multiintelligence tools is typically subjective which needs to be aligned with objective machine metrics.

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“…The most effective way of measuring image quality is human observation [32], because the purpose of the image is to convey information to a human observer -such as the Video National Imagery Interpretability Rating Scale (V-NIIRS) [33,34]. Unfortunately, current methods have inconvenient metrics in the field of image processing because these processes have yet to be fully automated [35].…”

Section: Introductionmentioning

confidence: 99%

Multi-focus and multi-modal fusion: a study of multi-resolution transforms

et al. 2016

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Automated image fusion has a wide range of applications across a multitude of fields such as biomedical diagnostics, night vision, and target recognition. Automation in the field of image fusion is difficult because there are many types of imagery data that can be fused using different multi-resolution transforms. The different image fusion transforms provide coefficients for image fusion, creating a large number of possibilities. This paper seeks to understand how automation could be conceived for selected the multiresolution transform for different applications, starting in the multifocus and multi-modal image sub-domains. The study analyzes the greatest effectiveness for each sub-domain, as well as identifying one or two transforms that are most effective for image fusion. The transform techniques are compared comprehensively to find a correlation between the fusion input characteristics and the optimal transform. The assessment is completed through the use of no-reference image fusion metrics including those of information theory based, image feature based, and structural similarity based methods.

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Application of VNIIRS for target tracking

Cited by 7 publications

References 79 publications

Prediction of compression-induced image interpretability degradation

Prediction of compression-induced image interpretability degradation

Multi-intelligence critical rating assessment of fusion techniques (MiCRAFT)

Multi-focus and multi-modal fusion: a study of multi-resolution transforms

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