Statistical modeling of SAR images: A survey
G Gao - Sensors, 2010 - mdpi.com
Statistical modeling is essential to SAR (Synthetic Aperture Radar) image interpretation. It
aims to describe SAR images through statistical methods and reveal the characteristics of …
aims to describe SAR images through statistical methods and reveal the characteristics of …
Image de-noising with machine learning: A review
Images are susceptible to various kinds of noises, which corrupt the pictorial information
stored in the images. Image de-noising has become an integral part of the image processing …
stored in the images. Image de-noising has become an integral part of the image processing …
Change detection in synthetic aperture radar images based on deep neural networks
This paper presents a novel change detection approach for synthetic aperture radar images
based on deep learning. The approach accomplishes the detection of the changed and …
based on deep learning. The approach accomplishes the detection of the changed and …
Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering
M Gong, Z Zhou, J Ma - IEEE Transactions on Image …, 2011 - ieeexplore.ieee.org
This paper presents an unsupervised distribution-free change detection approach for
synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy …
synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy …
Superpixel-level CFAR detectors for ship detection in SAR imagery
Synthetic aperture radar (SAR) is one of the most widely employed remote sensing
modalities for large-scale monitoring of maritime activity. Ship detection in SAR images is a …
modalities for large-scale monitoring of maritime activity. Ship detection in SAR images is a …
Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery
The availability of synthetic aperture radar (SAR) data offers great potential for
environmental monitoring due to the insensitiveness of SAR imagery to atmospheric and …
environmental monitoring due to the insensitiveness of SAR imagery to atmospheric and …
Multisensor image fusion and enhancement in spectral total variation domain
Most existing image fusion methods assume that at least one input image contains high-
quality information at any place of an observed scene. Thus, these fusion methods will fail if …
quality information at any place of an observed scene. Thus, these fusion methods will fail if …
Multiregion image segmentation by parametric kernel graph cuts
The purpose of this study is to investigate multiregion graph cut image partitioning via kernel
map** of the image data. The image data is transformed implicitly by a kernel function so …
map** of the image data. The image data is transformed implicitly by a kernel function so …
Ship classification and detection based on CNN using GF-3 SAR images
Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been
a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of …
a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of …
Coarse-to-fine contrastive self-supervised feature learning for land-cover classification in SAR images with limited labeled data
Contrastive self-supervised learning (CSSL) has achieved promising results in extracting
visual features from unlabeled data. Most of the current CSSL methods are used to learn …
visual features from unlabeled data. Most of the current CSSL methods are used to learn …