Object Segmentation for Linearly Polarimetric Passive Millimeter Wave Images Based on Principle Component Analysis

Lü, Xuan; Peng, Furong; Li, Guanghui; Zhang, Xiao; Hu, Taiyang

doi:10.2528/pierm17080804

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In some studies, color space mapping [13,20,29] is applied on fused multispectral polarimetric components and then fuzzy C-means clustering on separate objects from the background. Lu [59] applied PCA on different polarized images and then used a clustering technique to segment objects. These methods do not require any previous background model to segment the foreground/object from the background and are robust to illumination conditions.…”

Section: Segmentationmentioning

confidence: 99%

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

2020

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Multispectral Polarimetric Imagery (MSPI) contains significant information about an object’s distribution, shape, shading, texture and roughness features which can distinguish between foreground and background in a complex scene. Due to spectral signatures being limited to material properties, Background Segmentation (BS) is a difficult task when there are shadows, illumination and clutter in a scene. In this work, we propose a two-fold BS approach: multiband image fusion and polarimetric BS. Firstly, considering that the background in a scene is polarized by nature, the spectral reflectance and correlations and the textural features of MSPI are calculated and analyzed to demonstrate the fusion significance. After that, integrating Principal Component Analysis (PCA) with Fast Fourier Transform (FFT), a hybrid fusion technique is proposed to show the multiband fusion effectiveness. Secondly, utilizing the Stokes vector, polarimetric components are calculated to separate a complex scene’s background from its foreground by constructing four significant foreground masks. An intensity-invariant mask is built by differentiating between the median filtering versions of unpolarized and polarized images. A strongly unpolarized foreground mask is also constructed in two different ways, through analyzing the Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP). Moreover, a strongly polarized mask and a strong light intensity mask are also calculated based on the azimuth angle and the total light intensity. Finally, all these masks are combined, and a morphological operation is applied to segment the final background area of a scene. The proposed two-fold BS algorithm is evaluated using distinct statistical measurements and compared with well-known fusion methods and BS methods highlighted in this paper. The experimental results demonstrate that the proposed hybrid fusion method significantly improves multiband fusion quality. Furthermore, the proposed polarimetric BS approach also improves the mean accuracy, geometric mean and F1-score to 0.95, 0.93 and 0.97, respectively, for scenes in the MSPI dataset compared with those obtained from the methods in the literature considered in this paper. Future work will investigate mixed polarized and unpolarized BS in the MSPI dataset with specular reflection.

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Section: Segmentationmentioning

confidence: 99%

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

2020

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“…In previous work [11][12][13], we fixed a single pixel conical scanning radiometric imaging system at W-band. The scanning platform supports a maximum azimuth range of −170 • to 170 • and a maximum elevation range of −75 • to 75 • , covering almost the whole space.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Distortion Correction for Single Pixel Conical Scanning Radiometric Imaging System at W-Band

Lü¹,

Zhang²,

Hu³

2019

PIER Letters

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Conical scanning radiometric imaging system is good at large field view but suffers from visual nonlinear distortion. The distortion is caused by azimuth and elevation sampling in sphere coordinate, especially for short range and large views. An outdoor experiment is carried out on a building, and the raw image is obtained with obvious distortion. The key to correct distortion is solving the range in relationship between sphere coordinate and Cartesian coordinate. For a specific building, it is approximately treated as a plane object, and its height is assumed known to solve the range and parameters for plane fitting. Once the coordinates of all pixels are determined, the object is represented in Cartesian coordinate, and the nonlinear distortion is corrected. If any size information for object is unknown, an arbitrary plane is also competent for distortion correction. The difference is that the correcting result is a projection onto this plane instead of real location. However, the projection is also compatible with human vision.

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C-curve feature of complex permittivity estimation based on multi-polarization measurements in passive millimeter-wave sensing

Cheng

et al. 2019

Opt. Lett.

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Object Segmentation for Linearly Polarimetric Passive Millimeter Wave Images Based on Principle Component Analysis

Cited by 3 publications

References 16 publications

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

Hybrid Fusion-Based Background Segmentation in Multispectral Polarimetric Imagery

Nonlinear Distortion Correction for Single Pixel Conical Scanning Radiometric Imaging System at W-Band

C-curve feature of complex permittivity estimation based on multi-polarization measurements in passive millimeter-wave sensing

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