scholarly journals Mapping Oil Spills from Dual-Polarized SAR Images Using an Artificial Neural Network: Application to Oil Spill in the Kerch Strait in November 2007

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2237 ◽  
Author(s):  
Daeseong Kim ◽  
Hyung-Sup Jung
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Spill ◽  
Oil Spills ◽  
Sar Images ◽  
Kerch Strait ◽  
Network Application ◽  
Artificial Neural ◽  
Dual Polarized

scholarly journals Применение искусственной нейронной сети в экспериментах по ядерному спиновому эху

2019 ◽  
Vol 45 (12) ◽  
pp. 16
Author(s):  
С.Н. Полулях ◽  
А.И. Горбованов
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nuclear Spin ◽  
Spin Echo ◽  
Gaussian Form ◽  
The Neural Network ◽  
Network Application ◽  
Artificial Neural

The possibility of artificial neural network application to detect nuclear spin echo signals under conditions when the echo amplitude is comparable to the amplitude of the noise is demonstrated. Data obtained by superimposing the model echo signals of a Gaussian form on experimentally recorded noise signals is proposed to use for training the neural network.

Speckle Noise Filtering Using Back-Propagation Multi-Layer Perceptron Network in Synthetic Aperture Radar Image

2020 ◽  
pp. 489-510
Author(s):  
Khwairakpam Amitab ◽  
Debdatta Kandar ◽  
Arnab K. Maji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Speckle Noise ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Multi Layer Perceptron ◽  
Sar Images ◽  
Artificial Neural

Synthetic Aperture Radar (SAR) are imaging Radar, it uses electromagnetic radiation to illuminate the scanned surface and produce high resolution images in all-weather condition, day and night. Interference of signals causes noise and degrades the quality of the image, it causes serious difficulty in analyzing the images. Speckle is multiplicative noise that inherently exist in SAR images. Artificial Neural Network (ANN) have the capability of learning and is gaining popularity in SAR image processing. Multi-Layer Perceptron (MLP) is a feed forward artificial neural network model that consists of an input layer, several hidden layers, and an output layer. We have simulated MLP with two hidden layer in Matlab. Speckle noises were added to the target SAR image and applied MLP for speckle noise reduction. It is found that speckle noise in SAR images can be reduced by using MLP. We have considered Log-sigmoid, Tan-Sigmoid and Linear Transfer Function for the hidden layers. The MLP network are trained using Gradient descent with momentum back propagation, Resilient back propagation and Levenberg-Marquardt back propagation and comparatively evaluated the performance.

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