Defining a process to fuse polarimetric and spectral data for target detection and explore the trade space via simulation

Flusche, Brian M.; Gartley, Michael; Schott, John R.

doi:10.1117/1.3516616

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…While the spectral information tend to tell us about the distribution of material components in a scene, polarimetric information tells us about surface feature, shape, shading, and roughness [8] . However, both spectral and polarimetric detection systems may suffer from substantial false alarms and missed detection because of their respective background clutter [9] . Spectral-polarimetric imaging can provide complementary discriminative information, fusion of spectral and polarization information will result in better target detection and recognition performance [10,11] .…”

Section: Introductionmentioning

confidence: 99%

Camouflaged target separation by spectral-polarimetric imagery fusion with shearlet transform and clustering segmentation

Zhou

Liu

2013

SPIE Proceedings

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Camouflaged targets detection in complex background is a challenging problem. Spectral-polarimetric imaging can offers spectral information and polarization information from the objects in the scene. Fusion of the spectral and polarization information in the images will result in better camouflaged target identification and recognition. In this paper a novel spectral-polarimetric image fusion algorithm based on Shearlet transform is proposed. Firstly, every polarimetric image in each wave band is decomposed into images of low frequency components and high frequency components by Shearlet transform. Then, the fused low frequency approximate coefficients are obtained with weighted average method, and the fused high frequency coefficients are obtained with area-based feature selection method, so features and details from different spectral-polarimetric images are fused successfully. After that, the kernel fuzzy c-means clustering algorithm is used for camouflaged object separation from its background. Experimental results have shown that better identification performance was achieved.

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Section: Introductionmentioning

confidence: 99%

Camouflaged target separation by spectral-polarimetric imagery fusion with shearlet transform and clustering segmentation

Zhou

Liu

2013

SPIE Proceedings

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“…[18][19][20] Similarly, polarimetric imagery also is typically fused with hyperspectral imagery. [21][22][23] In contrast to the aforementioned research, which relies on the hyperspectral characterization of materials to distinguish material types, we combine passive polarimetric and active reflectivity features of the dual imaging architecture. The specific imaging capabilities we use include degree of linear polarization (DoLP) from passive polarimetric imaging, monostatic unidirectional reflectance (f r ) from lidar imaging, and viewing orientation ðθ; ϕÞ.…”

Section: Introductionmentioning

confidence: 99%

Hybrid passive polarimetric imager and lidar combination for material classification

et al. 2020

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We investigate the augmentation of active imaging with passive polarimetric imaging for material classification. Experiments are conducted to obtain a multimodal dataset of lidar reflectivity and polarimetric thermal self-emission measurements against a diverse set of material types. Using the assumption that active lidar imaging can provide high-resolution threedimensional spatial information, a known surface orientation is utilized to enable higher fidelity classification. Machine learning is applied to the dataset of monostatic lidar unidirectional reflectivity and passive longwave infrared degree of linear polarization features for material classification. The hybrid sensor technique can classify materials with 91.1% accuracy even with measurement noise resulting in a signal-to-noise ratio of only 6 dB. The application of the proposed technique is applicable for the classification of hidden objects or could assist existing spatial-based object classification. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Indeed differences between the two polarization states of reflected or scattered light can be used to enhance the image contrast and to detect the shape of objects with a better resolution [11]. This allows for example to distinguish man-made targets in natural or urban scenes [12,13]. However, polarization independent filters are usually required.…”

Section: Introductionmentioning

confidence: 99%

Free-standing guided-mode resonance band-pass filters: from 1D to 2D structures

Sakat¹,

Vincent²,

Ghenuche³

et al. 2012

Opt. Express

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We study experimentally and theoretically band-pass filters based on guided-mode resonances in free-standing metal-dielectric structures with subwavelength gratings. A variety of filters are obtained: polarizing filters with 1D gratings, and unpolarized or selective filters with 2D gratings, which are shown to behave as two crossed-1D structures. In either case, a high transmission (up to ≈ 79 %) is demonstrated, which represents an eight-fold enhancement compared to the geometrical transmission of the grating. We also show that the angular sensitivity strongly depends on the rotation axis of the sample. This behavior is explained with a detailed description of the guided-mode transmission mechanism.

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Defining a process to fuse polarimetric and spectral data for target detection and explore the trade space via simulation

Cited by 6 publications

References 28 publications

Camouflaged target separation by spectral-polarimetric imagery fusion with shearlet transform and clustering segmentation

Camouflaged target separation by spectral-polarimetric imagery fusion with shearlet transform and clustering segmentation

Hybrid passive polarimetric imager and lidar combination for material classification

Free-standing guided-mode resonance band-pass filters: from 1D to 2D structures

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