scholarly journals A Deep-Neural-Network-Based Hybrid Method for Semi-Supervised Classification of Polarimetric SAR Data

2019 ◽  
Author(s):  
Chi Liu ◽  
Wenzhi Liao ◽  
Heng-Chao Li ◽  
Shaoguang Huang ◽  
Wilfried Philips
Keyword(s):  
Neural Network ◽  
Hybrid Method ◽  
Supervised Classification ◽  
Deep Neural Network ◽  
Polarimetric Sar ◽  
Sar Data

scholarly journals An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network

2021 ◽  
Author(s):  
Anh Nguyen ◽  
Khoa Pham ◽  
Dat Ngo ◽  
Thanh Ngo ◽  
Lam Pham
Keyword(s):  
Neural Network ◽  
Supervised Classification ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Network Architectures ◽  
Activation Functions ◽  
Scene Classification ◽  
Learning Network ◽  
Deep Learning Network

This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network. These activation functions comprise of Rectified Linear Units (ReLU), Exponential Linear Unit (ELU), Scaled Exponential Linear Unit (SELU), Gaussian Error Linear Unit (GELU), and the Inverse Square Root Linear Unit (ISRLU). To evaluate, experiments over two deep learning network architectures integrating these activation functions are conducted. The first model, basing on Multilayer Perceptron (MLP), is evaluated with MNIST dataset to perform these activation functions.Meanwhile, the second model, likely VGGish-based architecture, is applied for Acoustic Scene Classification (ASC) Task 1A in DCASE 2018 challenge, thus evaluate whether these activation functions work well in different datasets as well as different network architectures.

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 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.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

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