Deep learning in remote sensing: A comprehensive review and list of resources
Central to the looming paradigm shift toward data-intensive science, machine-learning
techniques are becoming increasingly important. In particular, deep learning has proven to …
techniques are becoming increasingly important. In particular, deep learning has proven to …
Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
Deep learning meets SAR: Concepts, models, pitfalls, and perspectives
Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
FEC: A feature fusion framework for SAR target recognition based on electromagnetic scattering features and deep CNN features
The active recognition of interesting targets has been a vital issue for synthetic aperture
radar (SAR) systems. The SAR recognition methods are mainly grouped as follows …
radar (SAR) systems. The SAR recognition methods are mainly grouped as follows …
Deep convolutional highway unit network for SAR target classification with limited labeled training data
Z Lin, K Ji, M Kang, X Leng… - IEEE Geoscience and …, 2017 - ieeexplore.ieee.org
The deep convolutional neural network (CNN) has been widely used for target classification,
because it can learn highly useful representations from data. However, it is difficult to apply a …
because it can learn highly useful representations from data. However, it is difficult to apply a …
Mixed loss graph attention network for few-shot SAR target classification
Restricted by the observation condition, synthetic aperture radar (SAR) automatic target
classification based on deep learning usually suffers from insufficient training samples. To …
classification based on deep learning usually suffers from insufficient training samples. To …
A new algorithm for SAR image target recognition based on an improved deep convolutional neural network
F Gao, T Huang, J Sun, J Wang, A Hussain… - Cognitive Computation, 2019 - Springer
In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep
learning models, and enhance the learning of target features, we propose a novel deep …
learning models, and enhance the learning of target features, we propose a novel deep …
Rotation awareness based self-supervised learning for SAR target recognition with limited training samples
Z Wen, Z Liu, S Zhang, Q Pan - IEEE Transactions on Image …, 2021 - ieeexplore.ieee.org
The scattering signatures of a synthetic aperture radar (SAR) target image will be highly
sensitive to different azimuth angles/poses, which aggravates the demand for training …
sensitive to different azimuth angles/poses, which aggravates the demand for training …
New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis
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 …
which makes it very difficult to perform target recognition in SAR images. Inspired by great …
A survey on the applications of convolutional neural networks for synthetic aperture radar: Recent advances
In recent years, convolutional neural networks (CNNs) have drawn considerable attention
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …
for the analysis of synthetic aperture radar (SAR) data. In this study, major subareas of SAR …