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Fractional Gabor convolutional network for multisource remote sensing data classification

Xudong Zhao (UGent) , Ran Tao, Wei Li, Wilfried Philips (UGent) and Wenzhi Liao (UGent)
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Abstract
Remote sensing using multisensor platforms has been systematically applied for monitoring and optimizing human activities. Several advanced techniques have been developed to enhance and extract the spatially and spectrally semantic information in the hyperspectral image (HSI) and light detection and ranging (LiDAR) data processing and analysis. However, an abundance of redundant information and sometimes a lack of discriminative features reduce the efficiency and effectiveness of multisource classification methods. This article proposes a fractional Gabor convolutional network (FGCN), focusing on efficient feature fusion and comprehensive feature extraction. First, the proposed FGCN uses Octave convolution layers to perform multisource information fusion and preserve discriminative information. Second, fractional Gabor convolutional (FGC) layers are proposed to extract multiscale, multidirectional, and semantic change features. The completeness and discrimination of the multisource features using different FGC kernels are improved, which yield robust feature extraction against semantic changes. Finally, the fractional Gabor feature and spectral feature are combined with two weighting factors which can be learned during the network training. Experimental results and comparisons with state-of-the-art multisource classification methods indicate the effectiveness of the proposed FGCN. With the FGCN, we can obtain an 89.90x0025; overall accuracy on the challenging Muufl Gulfport (MUUFL) data set, with an improvement of 3x0025; over state-of-the-art methods.
Keywords
Electrical and Electronic Engineering, General Earth and Planetary Sciences, Feature extraction, Laser radar, Convolution, Hyperspectral imaging, Stacking, Semantics, Redundancy, Fractional Gabor convolutional network (FGCN), hyperspectral image (HSI), light detection and ranging (LiDAR), multisensor data fusion, HYPERSPECTRAL IMAGE CLASSIFICATION, LIDAR DATA, DATA FUSION, EXTINCTION PROFILES, WAVE-FORM, FRAMEWORK, FOREST, NOISE

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MLA
Zhao, Xudong, et al. “Fractional Gabor Convolutional Network for Multisource Remote Sensing Data Classification.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 60, 2022, doi:10.1109/tgrs.2021.3065507.
APA
Zhao, X., Tao, R., Li, W., Philips, W., & Liao, W. (2022). Fractional Gabor convolutional network for multisource remote sensing data classification. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 60. https://doi.org/10.1109/tgrs.2021.3065507
Chicago author-date
Zhao, Xudong, Ran Tao, Wei Li, Wilfried Philips, and Wenzhi Liao. 2022. “Fractional Gabor Convolutional Network for Multisource Remote Sensing Data Classification.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60. https://doi.org/10.1109/tgrs.2021.3065507.
Chicago author-date (all authors)
Zhao, Xudong, Ran Tao, Wei Li, Wilfried Philips, and Wenzhi Liao. 2022. “Fractional Gabor Convolutional Network for Multisource Remote Sensing Data Classification.” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60. doi:10.1109/tgrs.2021.3065507.
Vancouver
1.
Zhao X, Tao R, Li W, Philips W, Liao W. Fractional Gabor convolutional network for multisource remote sensing data classification. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. 2022;60.
IEEE
[1]
X. Zhao, R. Tao, W. Li, W. Philips, and W. Liao, “Fractional Gabor convolutional network for multisource remote sensing data classification,” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 60, 2022.
@article{8710624,
  abstract     = {{Remote sensing using multisensor platforms has been systematically applied for monitoring and optimizing human activities. Several advanced techniques have been developed to enhance and extract the spatially and spectrally semantic information in the hyperspectral image (HSI) and light detection and ranging (LiDAR) data processing and analysis. However, an abundance of redundant information and sometimes a lack of discriminative features reduce the efficiency and effectiveness of multisource classification methods. This article proposes a fractional Gabor convolutional network (FGCN), focusing on efficient feature fusion and comprehensive feature extraction. First, the proposed FGCN uses Octave convolution layers to perform multisource information fusion and preserve discriminative information. Second, fractional Gabor convolutional (FGC) layers are proposed to extract multiscale, multidirectional, and semantic change features. The completeness and discrimination of the multisource features using different FGC kernels are improved, which yield robust feature extraction against semantic changes. Finally, the fractional Gabor feature and spectral feature are combined with two weighting factors which can be learned during the network training. Experimental results and comparisons with state-of-the-art multisource classification methods indicate the effectiveness of the proposed FGCN. With the FGCN, we can obtain an 89.90x0025; overall accuracy on the challenging Muufl Gulfport (MUUFL) data set, with an improvement of 3x0025; over state-of-the-art methods.}},
  articleno    = {{5503818}},
  author       = {{Zhao, Xudong and Tao, Ran and Li, Wei and Philips, Wilfried and Liao, Wenzhi}},
  issn         = {{0196-2892}},
  journal      = {{IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING}},
  keywords     = {{Electrical and Electronic Engineering,General Earth and Planetary Sciences,Feature extraction,Laser radar,Convolution,Hyperspectral imaging,Stacking,Semantics,Redundancy,Fractional Gabor convolutional network (FGCN),hyperspectral image (HSI),light detection and ranging (LiDAR),multisensor data fusion,HYPERSPECTRAL IMAGE CLASSIFICATION,LIDAR DATA,DATA FUSION,EXTINCTION PROFILES,WAVE-FORM,FRAMEWORK,FOREST,NOISE}},
  language     = {{eng}},
  pages        = {{18}},
  title        = {{Fractional Gabor convolutional network for multisource remote sensing data classification}},
  url          = {{http://doi.org/10.1109/tgrs.2021.3065507}},
  volume       = {{60}},
  year         = {{2022}},
}

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