Change detection based on artificial intelligence: State-of-the-art and challenges
Change detection based on remote sensing (RS) data is an important method of detecting
changes on the Earth's surface and has a wide range of applications in urban planning …
changes on the Earth's surface and has a wide range of applications in urban planning …
[HTML][HTML] Polarimetric imaging via deep learning: A review
Polarization can provide information largely uncorrelated with the spectrum and intensity.
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …
Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields …
U-Net-LSTM: time series-enhanced lake boundary prediction model
Change detection of natural lake boundaries is one of the important tasks in remote sensing
image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons …
image interpretation. In an ordinary fully connected network, or CNN, the signal of neurons …
Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images
Z Lv, F Wang, G Cui, JA Benediktsson… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Land cover change detection (LCCD) using remote sensing images (RSIs) plays an
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …
An unsupervised remote sensing change detection method based on multiscale graph convolutional network and metric learning
As a fundamental application, change detection (CD) is widespread in the remote sensing
(RS) community. With the increase in the spatial resolution of RS images, high-resolution …
(RS) community. With the increase in the spatial resolution of RS images, high-resolution …
Change detection in synthetic aperture radar images using a dual-domain network
Change detection from synthetic aperture radar (SAR) imagery is a critical yet challenging
task. Existing methods mainly focus on feature extraction in the spatial domain, and little …
task. Existing methods mainly focus on feature extraction in the spatial domain, and little …
Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection
G Gao, Q Bai, C Zhang, L Zhang, L Yao - ISPRS Journal of …, 2023 - Elsevier
Influenced by the imaging mechanism, the occurrence of interference clutter in synthetic
aperture radar (SAR) renders the identification of false alarms using detectors challenging …
aperture radar (SAR) renders the identification of false alarms using detectors challenging …
Deep learning in visual tracking: A review
Deep learning (DL) has made breakthroughs in many computer vision tasks and also in
visual tracking. From the beginning of the research on the automatic acquisition of high …
visual tracking. From the beginning of the research on the automatic acquisition of high …
A new end-to-end multi-dimensional CNN framework for land cover/land use change detection in multi-source remote sensing datasets
ST Seydi, M Hasanlou, M Amani - Remote Sensing, 2020 - mdpi.com
The diversity of change detection (CD) methods and the limitations in generalizing these
techniques using different types of remote sensing datasets over various study areas have …
techniques using different types of remote sensing datasets over various study areas have …
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 …