Summary of methods in Wide-Area Motion Imagery (WAMI)

Blasch, Erik; Seetharaman, Guna; Suddarth, Steven C.; Palaniappan, Kannappan; Chen, Genshe; Ling, Haibin; Basharat, Arlsan

doi:10.1117/12.2052894

Cited by 22 publications

(9 citation statements)

References 34 publications

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“…Using the MIQE, we are interested in modeling the quality of the information from high-resolution full motion video or low-resolution wide area motion imagery [97,98,99] as depicted in Figure 9. Using the MIQE, the goal was to look at the differences between NIIRS and V-NIIRS for a target tracking scenario.…”

Section: V-niirs Assessmentmentioning

confidence: 99%

Application of VNIIRS for target tracking

Blasch¹,

Kahler²

2015

SPIE Proceedings

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The Motion Imagery Standards Board (MISB) has created the Video National Imagery Interpretability Rating Scale (V-NIIRS). VNIIRS extends NIIRS to scene characterization from streaming video to include object recognition of various targets for a given size. To apply VNIIRs for target tracking, there is a need to understand the operating conditions of the sensor type, environmental phenomenon, and target behavior (SET). In this paper, we explore VNIIRS for target tracking given the sensor resolution to support the relative tracking performance using track success. The relative assessment can be used in relation to the absolute target size associated with the VNIIRS. In a notional analysis, we determine the issues and capabilities of using VNIIRS video quality ratings to determine track success. The outcome of the trade study is an experiment to understand how to use VNIIRS can support target tracking evaluation.

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Section: V-niirs Assessmentmentioning

confidence: 99%

Application of VNIIRS for target tracking

Blasch¹,

Kahler²

2015

SPIE Proceedings

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“…The military adaptation of this imaging capability is called wide-area motion imagery (WAMI), wide-area surveillance (WAS), or widearea persistent surveillance (WAPS) (1). For transportation planning applications, similar methods have fallen under the generic umbrella of 1-s time-lapse aerial photography (TLAP).…”

Section: Wami Versus Tlapmentioning

confidence: 99%

Generating a Vehicle Trajectory Database from Time-Lapse Aerial Photography

Zhang

Jordan

Livshits

2016

Transportation Research Record: Journal of the Transportation R

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The advancing art of microsimulation modeling is driving a growing interest in vehicle trajectory databases. These can be rich sources of travel information, providing origin–destination patterns, path choices, travel times or delays, and lane changing behavior. These metrics have historically been difficult to acquire, and even with the approach of the big-data era, it remains a challenge to collect high-quality, granular vehicle trajectory data. This paper describes how use of state-of-the-practice time-lapse aerial photography (TLAP), acquired continuously for up to several hours at 1-s frame rates, can produce trajectory data sets with high granularity. These surveys involve the use of airborne digital cameras held in stationary positions about one mile above the ground, to record the movement of virtually all highway vehicles in defined study areas. In this paper, TLAP’s differences from similar military imaging capabilities, such as wide-area motion imagery, are discussed, to explain how TLAP can be affordable for highway traffic studies. Then local survey design and execution for a study in the Phoenix, Arizona, area are presented. Flight and photography survey planning, image alignment, and data extraction methods are discussed; and study output and graphics are presented near the end. Big data someday will likely be magically able to supply all the field data needed for complex analysis or simulation of specific study areas; for now, however, 1-s TLAP can provide many of those benefits.

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“…Another example is WAMI, which generates images over city-sized areas to enable monitoring of vehicle and pedestrian movements. 3,4 A typical WAMI image data size is over 144 megapixels (12;000 × 12;000 pixels), and the nextgeneration WAMI image data size will be in the level of 1.6 gigapixels (40;000 × 40;000 pixels). 5 To transmit the raw data to the users or processing units, either a wideband channel or a long-time interval is needed.…”

Section: Introductionmentioning

confidence: 99%

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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Summary of methods in Wide-Area Motion Imagery (WAMI)

Cited by 22 publications

References 34 publications

Application of VNIIRS for target tracking

Application of VNIIRS for target tracking

Generating a Vehicle Trajectory Database from Time-Lapse Aerial Photography

Prediction of compression-induced image interpretability degradation

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