scholarly journals CLASSIFICATION OF POLARIMETRIC-SAR DATA WITH NEURAL NETWORK USING COMBINED FEATURES EXTRACTED FROM SCATTERING MODELS AND TEXTURE ANALYSIS

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
Katmoko Ari Sambodo ◽  
Aniati Murni ◽  
Mahdi Kartasasmita ◽  
Mahdi Kartasasmita
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Network Architecture ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
Polarimetric Sar ◽  
Decomposition Model ◽  
Combined Features ◽  
Sar Data

This paper shows a study on an alternative method for classification of polarimetric-SAR data. The method is designed by integrating the comined features extracted from two scattering models(i.e., freeman decomposition model and cloud decomposition model) and textural analysis with distribution-free neural network classifier. The neural network classifier (wich is based on a feedforward back-propagation neural network architecture) properly exploits the information in the combined features for providing high accuracy classification result. The effectiveness of the proposed method is demonstrated using E-SAR polarimetric data acquired on the area of Penajam, East Kalimantan, Indonesia. Keywords: Polarimetric-SAR, scattering model, freeman decomposition, Cloude decomposition, texture analysis, feature extraction, classification, neural networks.

scholarly journals Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach

2012 ◽  
Vol 4 (11) ◽  
pp. 3571-3595 ◽  
Author(s):  
Gonzalo Pajares ◽  
Carlos López-Martínez ◽  
F. Sánchez-Lladó ◽  
Íñigo Molina
Keyword(s):  
Neural Network ◽  
Network Optimization ◽  
Hopfield Neural Network ◽  
Optimization Approach ◽  
Polarimetric Sar ◽  
Neural Network Optimization ◽  
Sar Data

scholarly journals Faint Object Classification Using Artificial Neural Networks

1994 ◽  
Vol 161 ◽  
pp. 249-252
Author(s):  
M. Serra-Ricart
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Faint Object ◽  
Artificial Neural ◽  
Astronomical Images

Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

Fuzzy rule based classification of polarimetric SAR data

2002 ◽  
Vol 6 (3) ◽  
pp. 217-232 ◽  
Author(s):  
Martin Hellmann ◽  
Gunther Jäger
Keyword(s):  
Fuzzy Rule ◽  
Polarimetric Sar ◽  
Rule Based ◽  
Sar Data

scholarly journals USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

2020 ◽  
pp. 104-117
Author(s):  
O.S. Amosov ◽  
◽  
S.G. Amosova ◽  
D.S. Magola ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Neural Network Architecture ◽  
Computer Attacks ◽  
Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

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