Deep learning for change detection in remote sensing images: Comprehensive review and meta-analysis
L Khelifi, M Mignotte - Ieee Access, 2020 - ieeexplore.ieee.org
Deep learning (DL) algorithms are considered as a methodology of choice for remote-
sensing image analysis over the past few years. Due to its effective applications, deep …
sensing image analysis over the past few years. Due to its effective applications, deep …
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 …
A hierarchical fusion sar image change-detection method based on hf-crf model
The mainstream methods for change detection in synthetic-aperture radar (SAR) images use
difference images to define the initial change regions. However, methods can suffer from …
difference images to define the initial change regions. However, methods can suffer from …
Difference-guided multiscale graph convolution network for unsupervised change detection in PolSAR images
D Xu, M Li, Y Wu, P Zhang, X Xin, Z Yang - Neurocomputing, 2023 - Elsevier
Image change detection is important in polarimetric synthetic aperture radar (PolSAR) image
analysis and interpretation. However, improving its accuracy is challenging because of the …
analysis and interpretation. However, improving its accuracy is challenging because of the …
Earth environmental monitoring using multi-temporal synthetic aperture radar: A critical review of selected applications
Microwave remote sensing has widely demonstrated its potential in the continuous
monitoring of our rapidly changing planet. This review provides an overview of state-of-the …
monitoring of our rapidly changing planet. This review provides an overview of state-of-the …
TCD-Net: A novel deep learning framework for fully polarimetric change detection using transfer learning
Due to anthropogenic and natural activities, the land surface continuously changes over
time. The accurate and timely detection of changes is greatly important for environmental …
time. The accurate and timely detection of changes is greatly important for environmental …
A classified adversarial network for multi-spectral remote sensing image change detection
Adversarial training has demonstrated advanced capabilities for generating image models.
In this paper, we propose a deep neural network, named a classified adversarial network …
In this paper, we propose a deep neural network, named a classified adversarial network …
Change Detection Approach for SAR Imagery Based on Arc-Tangential Difference Image and k-Means++
UH Atasever, MA Gunen - IEEE Geoscience and Remote …, 2021 - ieeexplore.ieee.org
In this letter, an unsupervised change detection (CD) approach based on arc-tangential
difference and-Means++ clustering is presented for synthetic aperture radar (SAR) remote …
difference and-Means++ clustering is presented for synthetic aperture radar (SAR) remote …
A multi-objective enhanced fruit fly optimization (MO-EFOA) framework for despeckling SAR images using DTCWT based local adaptive thresholding
ABSTRACT The importance of Satellite Aperture Radar (SAR) imagery systems is
increasing day-by-day in various field such as earth observation, hi-technology war …
increasing day-by-day in various field such as earth observation, hi-technology war …
End-to-end SAR deep learning imaging method based on sparse optimization
S Zhao, J Ni, J Liang, S Xiong, Y Luo - Remote Sensing, 2021 - mdpi.com
Synthetic aperture radar (SAR) imaging has developed rapidly in recent years. Although the
traditional sparse optimization imaging algorithm has achieved effective results, its …
traditional sparse optimization imaging algorithm has achieved effective results, its …