Three-Dimension Polarimetric Correlation Pattern Interpretation Tool and its Application

Li, Ming-Dian; Xiao, Shunping; Chen, Siwei

doi:10.1109/tgrs.2022.3222691

Cited by 13 publications

(3 citation statements)

References 38 publications

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“…Electromagnetic wave is an important carrier to obtain the characteristics of the target, of which polarization is an essential property. Polarimetric radar can obtain targets' full-polarization information, which is beneficial to the inversion of targets' physical properties [2][3][4]. However, the quad-polarization (quad-pol) mode incurs higher hardware costs and system complexity.…”

Section: Introductionmentioning

confidence: 99%

Semantic information guided semi-supervised compact ISAR image super-resolution

Li,

Dai,

Deng

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies

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Polarimetric inverse synthetic aperture radar (ISAR), with its ability to operate in all conditions, plays an important role in space surveillance. The compact polarimetric mode balances hardware complexity and polarimetric information, commonly equipped with ISAR systems. However, the generation of high-resolution ISAR images usually requires a large bandwidth and coherent integration angle, which is constrained by the equipment’s physical conditions. At present, supervised learning methods are often used for image super-resolution in computer vision. However, super-resolution performance is often hampered by the occurrence of artifacts and the inadequate consideration of low-frequency information in low-resolution image data. To address these limitations, this work presents a semantic information guided semi-supervised deep-learning method. This framework incorporates implicit neural representation to extract and better utilize information from low-resolution ISAR images. In addition, semantic and super-resolution information are integrated to regulate the training process. Datasets comprising images and semantic information of compact polarimetric ISAR for satellite targets are constructed. The proposed method yields more elaborate super-resolution results with fewer artifacts. Quantitative evaluations are also carried out using the Peak-Signal-to-Noise (PSNR) metric. Compared with the typical methods, the proposed approach achieves superior super-resolution performance, with a performance improvement of at least 1.394 dB.

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Section: Introductionmentioning

confidence: 99%

Semantic information guided semi-supervised compact ISAR image super-resolution

Li,

Dai,

Deng

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies

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show abstract

“…Chen et al proposed a set of oscillation parameters, including oscillation amplitude, oscillation center, angular frequency, and initial angle, to fully characterize the scattering behavior in the rotation domain [54]. Following that, they developed a visualization tool for PolSAR data investigation to exhibit polarimetric correlation value in terms of polarization ellipticity and orientation angles, which were applied in land cover classification and ship detection [55,56].…”

Section: Introductionmentioning

confidence: 99%

The Detection of Green Tide Biomass by Remote Sensing Images and In Situ Measurement in the Yellow Sea of China

Tian,

Wang,

Zhang

et al. 2023

Remote Sensing

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The world’s largest macroalgae bloom (also known as green tide) has been reported since the 29th Olympic Games in 2008, which is verified as the fast reproduction of floating green macroalgae (Ulva prolifera). It is helpful to assess the biomass of macroalgae for the government of marine environment protection. In this study, the synchronization cruise experiment was firstly introduced, which aimed to investigate the biomass evaluation of Ulva prolifera in the Yellow Sea of China. The Floating Algae Index by Polarimetric SAR image (FAIPS) was then proposed. Finally, the floating algae biomass evaluation model was demonstrated and verified, which showed an exponential relationship between FAIPS and wet biomass per area (kg/m2) of macroalgae. The model proposed in this paper can be used in the biomass assessment of floating algae in the presence of polarimetric SAR images, regardless of daylight and cloud coverage over the sea surface.

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“…It is acknowledged that quad-pol SAR data has superior performance compared to dual-pol SAR data in applications such as urban damage level mapping [3][4][5] and terrain classification [6][7][8][9]. Once the quad-pol SAR data can be reconstructed from the dual-pol SAR data, its target-detection and classification performance [22,23] will improve based on the well-established full-polarimetric SAR techniques [24][25][26][27][28][29][30][31][32][33]. However, to date, there are few published studies on quad-pol SAR data reconstruction from the dual-pol SAR mode (HH-VH or VV-HV SAR modes).…”

Section: Introductionmentioning

confidence: 99%

Quad-Pol SAR Data Reconstruction from Dual-Pol SAR Mode Based on a Multiscale Feature Aggregation Network

Deng,

Zhou,

et al. 2023

Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing applications due to its ability to obtain full-polarization information. Compared to the quad-pol SAR, the dual-pol SAR mode has a wider observation swath and is more common in most SAR systems. The goal of reconstructing quad-pol SAR data from the dual-pol SAR mode is to learn the contextual information of dual-pol SAR images and the relationships among polarimetric channels. This work is dedicated to addressing this issue, and a multiscale feature aggregation network has been established to achieve the reconstruction task. Firstly, multiscale spatial and polarimetric features are extracted from the dual-pol SAR images using the pretrained VGG16 network. Then, a group-attention module (GAM) is designed to progressively fuse the multiscale features extracted by different layers. The fused feature maps are interpolated and aggregated with dual-pol SAR images to form a compact feature representation, which integrates the high- and low-level information of the network. Finally, a three-layer convolutional neural network (CNN) with a 1 × 1 convolutional kernel is employed to establish the mapping relationship between the feature representation and polarimetric covariance matrices. To evaluate the quad-pol SAR data reconstruction performance, both polarimetric target decomposition and terrain classification are adopted. Experimental studies are conducted on the ALOS/PALSAR and UAVSAR datasets. The qualitative and quantitative experimental results demonstrate the superiority of the proposed method. The reconstructed quad-pol SAR data can better sense buildings’ double-bounce scattering changes before and after a disaster. Furthermore, the reconstructed quad-pol SAR data of the proposed method achieve a 97.08% classification accuracy, which is 1.25% higher than that of dual-pol SAR data.

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Three-Dimension Polarimetric Correlation Pattern Interpretation Tool and its Application

Cited by 13 publications

References 38 publications

Semantic information guided semi-supervised compact ISAR image super-resolution

Semantic information guided semi-supervised compact ISAR image super-resolution

The Detection of Green Tide Biomass by Remote Sensing Images and In Situ Measurement in the Yellow Sea of China

Quad-Pol SAR Data Reconstruction from Dual-Pol SAR Mode Based on a Multiscale Feature Aggregation Network

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