Unsupervised Classification of Spectropolarimetric Data by Region-Based Evidence Fusion

Zhao, Yongqiang; Zhang, Guohua; Jie, Feiran; Gao, Shibo; Chen, Chao

doi:10.1109/lgrs.2011.2107726

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Images are displayed, collected, and saved within each band before transitioning to the next. The entire experimental process takes in approximately 1 min [45,46]. The third device involves data acquisition through window-scan CSBFTIS (Cross-Scanning Beam Fourier Transform Imaging Spectroscopy).…”

Section: Spectral-polarization Imaging Theorymentioning

confidence: 99%

“…To further validate the applicability and robustness of our algorithm, experiments were conducted utilizing the online public dataset released by Zhao from Northwestern Polytechnical University. The dataset was obtained using the LCTF polarization spectrum imaging method, using the specific acquisition and processing process detailed in reference [45,46]. The original dataset comprises 33 images in four polarization channels, covering wavelengths from 400 to 720 nm with 10 nm intervals.…”

Section: Online Public Datasetsmentioning

confidence: 99%

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Multi-Dimensional Fusion of Spectral and Polarimetric Images Followed by Pseudo-Color Algorithm Integration and Mapping in HSI Space

Guo,

Zhu,

Huang

et al. 2024

Remote Sensing

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Spectral–polarization imaging technology plays a crucial role in remote sensing detection, enhancing target identification and tracking capabilities by capturing both spectral and polarization information reflected from object surfaces. However, the acquisition of multi-dimensional data often leads to extensive datasets that necessitate comprehensive analysis, thereby impeding the convenience and efficiency of remote sensing detection. To address this challenge, we propose a fusion algorithm based on spectral–polarization characteristics, incorporating principal component analysis (PCA) and energy weighting. This algorithm effectively consolidates multi-dimensional features within the scene into a single image, enhancing object details and enriching edge features. The robustness and universality of our proposed algorithm are demonstrated through experimentally obtained datasets and verified with publicly available datasets. Additionally, to meet the requirements of remote sensing tracking, we meticulously designed a pseudo-color mapping scheme consistent with human vision. This scheme maps polarization degree to color saturation, polarization angle to hue, and the fused image to intensity, resulting in a visual display aligned with human visual perception. We also discuss the application of this technique in processing data generated by the Channel-modulated static birefringent Fourier transform imaging spectropolarimeter (CSBFTIS). Experimental results demonstrate a significant enhancement in the information entropy and average gradient of the fused image compared to the optimal image before fusion, achieving maximum increases of 88% and 94%, respectively. This provides a solid foundation for target recognition and tracking in airborne remote sensing detection.

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Section: Spectral-polarization Imaging Theorymentioning

confidence: 99%

Section: Online Public Datasetsmentioning

confidence: 99%

Multi-Dimensional Fusion of Spectral and Polarimetric Images Followed by Pseudo-Color Algorithm Integration and Mapping in HSI Space

Guo,

Zhu,

Huang

et al. 2024

Remote Sensing

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“…But HSI is unavoidably corrupted by various noises, e.g., Gaussian noise, mixed Poisson-Gaussian noise, dead-lines, and stripes. The noise will influence the subsequent processing, such as classification [3][4][5][6][7], segmentation [8], unmixing [9,10], object detection [11,12], background subtraction [13], and super-resolution [14]. The central limit theorem establishes that the composite effect of many independent noise sources (e.g., thermal noise, shot noise, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…From a practical point of view, current imaging systems designed based on the assumption of additive Gaussian noise perform quite well. As a kind of signal independent noise, the Gaussian assumption has been broadly used in HSI denoising [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. From a theoretical point of view, Gaussian noise is the worst-case scenario for additive noise as the Gaussian distribution maximizes the entropy subject to a variance constraint [15].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation

et al. 2019

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A hyperspectral image (HSI) contains abundant spatial and spectral information, but it is always corrupted by various noises, especially Gaussian noise. Global correlation (GC) across spectral domain and nonlocal self-similarity (NSS) across spatial domain are two important characteristics for an HSI. To keep the integrity of the global structure and improve the details of the restored HSI, we propose a global and nonlocal weighted tensor norm minimum denoising method which jointly utilizes GC and NSS. The weighted multilinear rank is utilized to depict the GC information. To preserve structural information with NSS, a patch-group-based low-rank-tensor-approximation (LRTA) model is designed. The LRTA makes use of Tucker decompositions of 4D patches, which are composed of a similar 3D patch group of HSI. The alternating direction method of multipliers (ADMM) is adapted to solve the proposed models. Experimental results show that the proposed algorithm can preserve the structural information and outperforms several state-of-the-art denoising methods.

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“…The measurement of Fresnel reflection coefficients in multiple bands can quantitatively assess the conductive characteristics to retrieve the dielectric constants, which provides good detectability of conductors and insulators. Roughness and surface orientation can be reflected in the spectral polarization parameters, which is critical for inhomogeneous objects identification [14]. Multiband polarization imaging has demonstrated enhanced target detection and navigation in military applications [15,16].…”

Section: Introductionmentioning

confidence: 99%

Multiband Polarization Imaging

et al. 2016

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Multiband polarization imaging is an emerging sensing method that enables simultaneous acquisition of multiband spectral and multiangle polarization information of an object of interest in the scene. Spectral signatures of the light reflected from a target reveal the characteristics of the material composing the target while polarized light provides useful information on the surface features such as light scattering and specular reflection. In multiband spectral imaging, combined spectral and polarization information offers a comprehensive representation of an object utilizing complementary spectral and polarization information in visual sensing. Multiband polarization imaging has demonstrated a potential in the recognition of targets in challenging operating environments such as low-contrast and hazy conditions. This paper presents the concept and recent advances of multiband polarization imaging techniques, in particular, a bioinspired multiband polarization vision system. Applications of multiband polarization imaging in various fields include atmospheric observation, object detection and classification, medical diagnostics, surveillance, and 3D object reconstruction.

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Unsupervised Classification of Spectropolarimetric Data by Region-Based Evidence Fusion

Cited by 9 publications

References 22 publications

Multi-Dimensional Fusion of Spectral and Polarimetric Images Followed by Pseudo-Color Algorithm Integration and Mapping in HSI Space

Multi-Dimensional Fusion of Spectral and Polarimetric Images Followed by Pseudo-Color Algorithm Integration and Mapping in HSI Space

Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation

Multiband Polarization Imaging

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