Self-supervised learning in remote sensing: A review
Y Wang, CM Albrecht, NAA Braham… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …
triggering interest within both the computer vision and remote sensing communities. While …
Self-supervised remote sensing feature learning: Learning paradigms, challenges, and future works
Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …
sensing images (RSIs). To better understand the connection between three feature learning …
Speckle2Void: Deep self-supervised SAR despeckling with blind-spot convolutional neural networks
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by
speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis …
speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis …
Deep learning methods for synthetic aperture radar image despeckling: An overview of trends and perspectives
Synthetic aperture radar (SAR) images are affected by a spatially correlated and signal-
dependent noise called speckle, which is very severe and may hinder image exploitation …
dependent noise called speckle, which is very severe and may hinder image exploitation …
Nonlocal CNN SAR image despeckling
We propose a new method for SAR image despeckling, which performs nonlocal filtering
with a deep learning engine. Nonlocal filtering has proven very effective for SAR …
with a deep learning engine. Nonlocal filtering has proven very effective for SAR …
Image denoising and despeckling methods for SAR images to improve image enhancement performance: A survey
E Ponmani, P Saravanan - Multimedia Tools and Applications, 2021 - search.proquest.com
The synthetic aperture radar (SAR) images are playing an essential role in remote sensing.
Various types of internal, external, and environmental noise are affecting the SAR images …
Various types of internal, external, and environmental noise are affecting the SAR images …
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 …
Edge Constrained Guided Feature Perception Network for Ship Detection in SAR Images
S Xu, J Fan, X Jia, J Chang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
The ship target detection technology in synthetic aperture radar (SAR) imaging plays an
important role in information warfare. However, due to the coherence of the system and the …
important role in information warfare. However, due to the coherence of the system and the …
Sentinel-1 Dual-polarization SAR Images Despeckling Network Based on Unsupervised Learning
Supervised deep learning despeckling methods usually use optical images to simulate
multiplicative noise for training. However, due to the different imaging mechanisms of optical …
multiplicative noise for training. However, due to the different imaging mechanisms of optical …
Self-supervised despeckling algorithm with an enhanced U-net for synthetic aperture radar images
G Zhang, Z Li, X Li, S Liu - Remote Sensing, 2021 - mdpi.com
Self-supervised method has proven to be a suitable approach for despeckling on synthetic
aperture radar (SAR) images. However, most self-supervised despeckling methods are …
aperture radar (SAR) images. However, most self-supervised despeckling methods are …