Cosine Visibility Extension of 1-D Mirrored Aperture Synthesis by a CNN for Spatial Resolution Enhancement

Zhao, Guanghui; Li, Qingxia; Lei, Zhenyu; Xiao, Chengwang; Chen, Zhiwei; Huang, Yuhang

doi:10.1109/lgrs.2023.3238051

Cited by 1 publication

(2 citation statements)

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“…The difference spectrum signal is, in fact, a spatial frequency spectrum signal, and its distribution characteristics bear some resemblance to the visibility signal [17] and the cosine visibility signal [18]. Consequently, similar to the VE method and the CVE method, the neural network model employed in the SE method is predominantly composed of CNN architectures [21,22].…”

Section: Se-cnnmentioning

confidence: 99%

“…In existing relevant studies, the visibility extension (VE) method [17] has not only improved the spatial resolution of real-aperture synthetic microwave radiometers, but has also reduced the reconstruction error of BTs. Likewise, the cosine visibility extension (CVE) method [18], while enhancing the spatial resolution of mirrored-aperture synthetic microwave radiometers, has also led to a decrease in the reconstruction error of BTs. The present study in this paper is inspired by these methods.…”

Section: Introductionmentioning

confidence: 99%

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Spectrum Extension of a Real-Aperture Microwave Radiometer Using a Spectrum Extension Convolutional Neural Network for Spatial Resolution Enhancement

Zhao,

Huang,

Xiao

et al. 2023

Remote Sensing

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Enhancing the spatial resolution of real-aperture microwave radiometers is an essential research topic. The accuracy of the numerical values of brightness temperatures (BTs) observed using microwave radiometers directly affects the precision of the retrieval of marine environmental parameters. Hence, ensuring the accuracy of the enhanced brightness temperature values is of paramount importance when striving to enhance spatial resolution. A spectrum extension (SE) method is proposed in this paper, which restores the suppressed high-frequency components in the scene BT spectrum through frequency domain transformation and calculations, specifically, dividing the observed BT spectrum by the conjugate of the antenna pattern spectrum and applying a Taylor approximation to suppress error amplification, thereby extending the observed BT spectrum. By using a convolutional neural network to correct errors in the calculated spectrum and then reconstructing the BT through inverse fast Fourier transform (IFFT), the enhanced BTs are obtained. Since the extended BT spectrum contains more high-frequency components, namely, the spectrum is closer to that of the original scene BT, the reconstructed BT not only achieves an enhancement in spatial resolution, but also an improvement in the accuracy of BT values. Both the results from simulated data and satellite-measured data processing illustrate that the SE method is able to enhance the spatial resolution of real-aperture microwave radiometers and concurrently improve the accuracy of BT values.

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