SAR image segmentation based on convolutional-wavelet neural network and Markov random field
Synthetic aperture radar (SAR) imaging system is usually an observation of the earths'
surface. It means that rich structures exist in SAR images. Convolutional neural network …
surface. It means that rich structures exist in SAR images. Convolutional neural network …
Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19
COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now,
a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X …
a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X …
Detecting anomalous traffic in the controlled network based on cross entropy and support vector machine
W Han, J Xue, H Yan - IET Information Security, 2019 - Wiley Online Library
Network anomaly detection is an effective way for analysing and detecting malicious attacks.
However, the typical anomaly detection techniques cannot perform the desired effect in the …
However, the typical anomaly detection techniques cannot perform the desired effect in the …
A thumbnail-based hierarchical fuzzy clustering algorithm for SAR image segmentation
This paper proposes a novel algorithm for segmentation of synthetic aperture radar (SAR)
image, our proposed algorithm (THFCM) is based on thumbnail representations and a …
image, our proposed algorithm (THFCM) is based on thumbnail representations and a …
Practical issue analyses and imaging approach for hypersonic vehicle-borne SAR with near-vertical diving trajectory
S Tang, X Zhang, Z He, Z Chen, W Du… - … on Geoscience and …, 2023 - ieeexplore.ieee.org
As a frontier technology in radar imaging, hypersonic vehicle (HSV)-borne synthetic aperture
radar (SAR) has several practical issues to be dealt with, namely, ground resolution …
radar (SAR) has several practical issues to be dealt with, namely, ground resolution …
The Geodesic Distance between Models and its Application to Region Discrimination
J Naranjo-Torres, J Gambini… - IEEE Journal of Selected …, 2017 - ieeexplore.ieee.org
The GI 0 distribution is able to characterize different regions in monopolarized SAR imagery.
It is indexed by three parameters: the number of looks (which can be estimated in the whole …
It is indexed by three parameters: the number of looks (which can be estimated in the whole …
Water body segmentation of synthetic aperture radar image using deep convolutional neural networks
Abstract Deep Convolutional Neural Networks are finding their way into modern machine
learning tasks and proved themselves to become one of the best contenders for future …
learning tasks and proved themselves to become one of the best contenders for future …
Entropy estimators in SAR image classification
Remotely sensed data are essential for understanding environmental dynamics, for their
forecasting, and for early detection of disasters. Microwave remote sensing sensors …
forecasting, and for early detection of disasters. Microwave remote sensing sensors …
Shannon Entropy for the Model: A New Segmentation Approach
JA Ferreira, ADC Nascimento - IEEE Journal of Selected Topics …, 2020 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) has been successfully used as a remote sensing tool.
However, SAR images are contaminated by speckle noise and require specialized …
However, SAR images are contaminated by speckle noise and require specialized …
Statistically Principled Deep Learning for SAR Image Segmentation
C Goldberg - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
This paper proposes a novel approach for Synthetic Aperture Radar (SAR) image
segmentation by incorporating known statistical properties of SAR into deep learning …
segmentation by incorporating known statistical properties of SAR into deep learning …