Baseline processing pipeline for fast automatic target detection and recognition in airborne 3D ladar imagery

Roy, Sougata; Maheux, Jean

doi:10.1117/12.883287

Cited by 7 publications

(8 citation statements)

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“…Considering the aforementioned processing gain due to the platform, the coding differences and the desired this latency, we set an upper processing time limit of 500ms for our developing platform. The literature suggests measuring the computational complexity in seconds [26], [28], [29]. But due to the processing time limit set and the high-speed the missile is flying at, we set the computational complexity on a millisecond basis [30].…”

Section: Methodsmentioning

confidence: 99%

“…To the best of our knowledge, the only open source military oriented ATR algorithms are based on Spin Images [26], geometric fitting [27], multi-hypothesis sequential testing [28], the Baseline Processing Pipeline [29] and the Projection Density Energy based solution [30]. Although Spin Images perform well in target recognition, their calculation time exceeds the constraints of a LIDAR based missile.…”

Section: Related Workmentioning

confidence: 99%

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3D automatic target recognition for future LIDAR missiles

Kechagias‐Stamatis

Aouf

Richardson

2016

IEEE Trans. Aerosp. Electron. Syst.

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We present a real-time 3D Automatic Target Recognition approach appropriate for future Light Detection and Ranging (LIDAR) based missiles. Our technique extends the Speeded-Up Robust Features method into the third dimension by solving multiple 2-dimensional problems and performs template matching based on the extreme case of a single pose per target. Evaluation on military targets shows higher recognition rates under various transformations and perturbations at lower processing time compared to state-of-the-art approaches.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

3D automatic target recognition for future LIDAR missiles

Kechagias‐Stamatis

Aouf

Richardson

2016

IEEE Trans. Aerosp. Electron. Syst.

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“…Then, for each c P acting as a centroid, we extract a spherical Figure 5. Despite the effectiveness of a smooth surface filtering scheme, surprisingly, current military literature either does not exploit such a procedure at all [15] or discards only planar ground surfaces [18,23].…”

Section: Smooth Surface Filteringmentioning

confidence: 99%

“…It is worth noting that literature has not investigated yet how atmospheric disturbances affect ATR tasks. Additionally, opposing to current 3D descriptor evaluations [13,14] or to purely military oriented 3D ATR manuscripts [15][16][17][18] that consider a constant target scale, we investigate the descriptor's performance in various missile-target ranges. It should be noted that, in contrast to current literature that correlates scale with the description radius [14,19] we refer to its distance related meaning that affects the spot size of the laser beam on the scene and forces the scene to have simultaneously a different size and resolution.…”

Section: Introductionmentioning

confidence: 99%

Evaluating 3D local descriptors for future LIDAR missiles with automatic target recognition capabilities

Kechagias‐Stamatis

Aouf

2017

The Imaging Science Journal

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Evaluating 3D Local Descriptors for Future LIDAR Missiles with Automatic Target Recognition Capabilities Future Light Detection and Ranging seeker missiles incorporating 3D Automatic Target Recognition (ATR) capabilities can improve the missile's effectiveness in complex battlefield environments. Considering the progress of local 3D descriptors in the computer vision domain, this paper evaluates a number of these on highly credible simulated air-to-ground missile engagement scenarios. The latter take into account numerous parameters that have not been investigated yet by the literature including variable missile-target range, 6-Degrees-of-Freedom missile motion and atmospheric disturbances. Additionally, the evaluation process utilizes our suggested 3D ATR architecture that compared to current pipelines involves more post-processing layers aiming at further enhancing 3D ATR performance. Our trials reveal that computer vision algorithms are appealing for missile oriented 3D ATR.

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“…Various studies have been conducted on this front, among them are filtering algorithms [2][3][4], parametric system identification [5], statistical signal processing [6], subspace methods [7][8][9], entropy-based approaches [10] and techniques employing parametric and non-parametric modeling [11]. A neural networks approach has been introduced in the marine radar application [12] for sea clutter reduction and other approaches have been successfully used in automatic radar detection in ground clutter [13,14]. While each method tackles different aspects of the problem, there is still room for further improvement.…”

Section: Introductionmentioning

confidence: 99%

GPR Signal Enhancement Using Sliding-Window Space-Frequency Matrices

Yavuz¹,

Fouda²,

Teixeira³

2014

PIER

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Abstract-Ground penetrating radar (GPR) has shown to provide useful results for detection of buried objects. However, its performance suffers from strong reflection from ground surface especially for shallowly buried targets. In such cases, the detection problem depends on the separation of the target signal from the ground backscatter such as landmines and unexploded ordnances. In this paper, we discuss and analyze the use of space-frequency time-reversal matrices for the enhancement of ground penetrating radar signals and potential clutter reduction. Through the use of sliding windows, submatrices from a given B-scan (radargram) are utilized to extract localized scattering information of a given detection scenario. Each sub-B-scan is decomposed to its singular vectors and later used to render synthetic aperture time-domain singular vector distributions corresponding to different scattering mechanisms. Later, they are weighted by the singular values and subtracted from the full B-scan to achieve reduced clutter and enhanced target response. The method shows satisfactory results for shallowly buried dielectric targets even in the presence of rough surface profiles.

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Baseline processing pipeline for fast automatic target detection and recognition in airborne 3D ladar imagery

Cited by 7 publications

References 0 publications

3D automatic target recognition for future LIDAR missiles

3D automatic target recognition for future LIDAR missiles

Evaluating 3D local descriptors for future LIDAR missiles with automatic target recognition capabilities

GPR Signal Enhancement Using Sliding-Window Space-Frequency Matrices

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