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Published in 2021 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2021.1953719
Abstract: Synthetic Aperture Radar (SAR) images are prone to be contaminated by noise, which makes it very difficult to perform target recognition in SAR images. Inspired by great success of very deep convol...
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Keywords:
target recognition;
sar target;
new sar;
sar ... See more keywords
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Published in 2020 at "Remote Sensing Letters"
DOI: 10.1080/2150704x.2020.1773564
Abstract: ABSTRACT This letter develops a synthetic aperture radar (SAR) target classification method based on bidimensional variational mode decomposition (BVMD) and multitask compressive sensing (MTCS). BVMD is employed to decompose SAR images to exploit the time-frequency…
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Keywords:
sar;
bvmd;
multitask compressive;
target classification ... See more keywords
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2
Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3193773
Abstract: Deep learning methods have achieved state-of-the-art performance on synthetic aperture radar (SAR) target recognition tasks in recent years. However, obtaining sufficient SAR images for training these deep learning methods is costly in time and labor.…
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Keywords:
recognition;
sar target;
target recognition;
model ... See more keywords
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Published in 2021 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2021.3104267
Abstract: Deep convolutional neural networks (CNNs) have yielded unusually brilliant results in synthetic aperture radar (SAR) target recognition. However, overparameterization is a widely-recognized property of deep CNNs, and most previous works excessively pursued high accuracy but…
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Keywords:
knowledge;
target recognition;
network;
sar target ... See more keywords
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Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3206901
Abstract: The convolutional neural network (CNN) is widely used in synthetic aperture radar (SAR) target recognition, but conventional CNN mainly adopts a single-scale convolutional kernel, resulting in losing part of the feature information of targets and…
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Keywords:
classification;
sar target;
super class;
class ... See more keywords
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1
Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3218369
Abstract: Sufficient synthetic aperture radar (SAR) target images are very important for the development of research works. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this…
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Keywords:
network;
target images;
sar target;
target image ... See more keywords
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Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3055643
Abstract: In article [1], after drawing the curve of Fig. 4, we mistakenly marked two coordinate values in the fourth column of the figure to be the same as those in the third column. We modified…
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Keywords:
sar target;
wavelet srnet;
erratum multilevel;
target recognition ... See more keywords
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1
Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3132020
Abstract: Deep learning models have achieved remarkable performance in synthetic aperture radar (SAR) target recognition. However, the accuracy of these methods is sensitive to the hyper-parameters and the traditional backpropagation is time consuming. In this letter,…
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Keywords:
recognition;
network;
sar target;
modified convolutional ... See more keywords
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Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3141389
Abstract: Due to the difficulties of obtaining sufficient real synthetic aperture radar (SAR) images, introducing simulated images can effectively enrich the training dataset in SAR target recognition. This letter explores how to accurately identify the targets…
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Keywords:
recognition;
similarity;
sar target;
using simulated ... See more keywords
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Published in 2021 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3106915
Abstract: It is well-known that the convolutional neural network (CNN) is an effective method for synthetic aperture radar (SAR) target classification. In the convolutional layer of CNN, convolutional kernels of different sizes can extract different feature…
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Keywords:
classification;
feature;
target;
sar target ... See more keywords
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Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2023.3267480
Abstract: Deep-learning-based target recognition in synthetic aperture radar (SAR) images has been actively studied in recent years. However, it is very costly to collect large numbers of labeled SAR images, especially measured SAR target images of…
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Keywords:
target images;
target;
sar target;
measured sar ... See more keywords