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.

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

2016 ◽  
pp. 280-301
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.

scholarly journals Convolutional Neural Network with a Learnable Spatial Activation Function for SAR Image Despeckling and Forest Image Analysis

2021 ◽  
Vol 13 (17) ◽  
pp. 3444
Author(s):  
Hao Wang ◽  
Zhendong Ding ◽  
Xinyi Li ◽  
Shiyu Shen ◽  
Xiaodong Ye ◽  
...  
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Network ◽  
Speckle Noise ◽  
Activation Function ◽  
Google Earth ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Image Despeckling

Synthetic aperture radar (SAR) images are often disturbed by speckle noise, making SAR image interpretation tasks more difficult. Therefore, speckle suppression becomes a pre-processing step. In recent years, approaches based on convolutional neural network (CNN) achieved good results in synthetic aperture radar (SAR) images despeckling. However, these CNN-based SAR images despeckling approaches usually require large computational resources, especially in the case of huge training data. In this paper, we proposed a SAR image despeckling method using a CNN platform with a new learnable spatial activation function, which required significantly fewer network parameters without incurring any degradation in performance over the state-of-the-art despeckling methods. Specifically, we redefined the rectified linear units (ReLU) function by adding a convolutional kernel to obtain the weight map of each pixel, making the activation function learnable. Meanwhile, we designed several experiments to demonstrate the advantages of our method. In total, 400 images from Google Earth comprising various scenes were selected as a training set in addition to 10 Google Earth images including athletic field, buildings, beach, and bridges as a test set, which achieved good despeckling effects in both visual and index results (peak signal to noise ratio (PSNR): 26.37 ± 2.68 and structural similarity index (SSIM): 0.83 ± 0.07 for different speckle noise levels). Extensive experiments were performed on synthetic and real SAR images to demonstrate the effectiveness of the proposed method, which proved to have a superior despeckling effect and higher ENL magnitudes than the existing methods. Our method was applied to coniferous forest, broad-leaved forest, and conifer broad-leaved mixed forest and proved to have a good despeckling effect (PSNR: 23.84 ± 1.09 and SSIM: 0.79 ± 0.02). Our method presents a robust framework inspired by the deep learning technology that realizes the speckle noise suppression for various remote sensing images.

scholarly journals Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qingyan Meng ◽  
Linlin Zhang ◽  
Qiuxia Xie ◽  
Shun Yao ◽  
Xu Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Landsat 8 ◽  
Combined Use ◽  
Artificial Neural ◽  
Agricultural Areas ◽  
Aperture Radar

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

scholarly journals Artificial Neural Network (ANN) based Object Recognition Using Multiple Feature Sets

2011 ◽  
pp. 32-36
Author(s):  
Manami Barthakur ◽  
Tapashi Thakuria ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Multi Layer Perceptron ◽  
Multiple Features ◽  
Error Back Propagation ◽  
Multiple Feature ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning.

scholarly journals ACOUSTIC CLASSIFICATION OF FRESHWATER FISH SPECIES USING ARTIFICIAL NEURAL NETWORK: EVALUATION OF THE MODEL PERFORMANCE

2013 ◽  
Vol 19 (1) ◽  
pp. 19
Author(s):  
Zulkarnaen Fahmi ◽  
Wijopriono Wijopriono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fish Species ◽  
Back Propagation ◽  
Model Performance ◽  
Target Strength ◽  
Multi Layer Perceptron ◽  
Back Scattering ◽  
Artificial Neural ◽  
Scattering Strength

Hydroacoustic techniques are a valuable tool for the stock assessments of many fish species. Nonetheless, such techniques are limited by problems of species identification. Several methods and techniques have been used in addressing the problem of acoustic identification species and one of them is Artificial Neural Networks (ANNs). In this paper, Back propagation (BP) and Multi Layer Perceptron (MLP) of the Artificial Neural Network were used to classify carp (<em>Cyprinus carpio</em>), tilapia (<em>Oreochromis niloticus</em>), and catfish (<em>Pangasius hypothalmus</em>). Classification was done using a set of descriptors extracted from the acoustic data records, i.e. Volume Back scattering (Sv), Target Strength (TS), Area Back scattering Strength, Skewness, Kurtosis, Depth, Height and Relative altitude. The results showed that the Multi Layer Perceptron approach performed better than the Back propagation. The classification rates was 85.7% with the multi layer perceptron (MLP) compared to 84.8% with back propagation (BP) ANN.

scholarly journals SAR Image Despeckling with Adaptive Multiscale Products Based on Directionlet Transform

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Yixiang Lu ◽  
Qingwei Gao ◽  
Dong Sun ◽  
Dexiang Zhang
Keyword(s):  
Synthetic Aperture Radar ◽  
Speckle Noise ◽  
Experimental Results ◽  
Synthetic Aperture ◽  
Synthetic Image ◽  
Sar Image ◽  
Sar Images ◽  
Coherent Wave ◽  
Multiscale Products ◽  
Suppress Noise

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise generated by radar coherent wave. In this paper, a new despeckling algorithm based on directionlets using multiscale products is proposed. We first take an anisotropic directionlet transform on the logarithmically transformed SAR images and multiply the coefficients at adjacent scales to enhance the details of image under consideration. Then, different from traditional thresholding methods, a threshold is applied to the multiscale products of the directionlet coefficients to suppress noise. Since the multiplication amplifies the significant features of signal and dilute noise, the proposed method reduces noise effectively while preserving edge structures. Finally, we compare the performance of the proposed algorithm with other despeckling methods applied to synthetic image and real SAR images. Experimental results demonstrate the effectiveness of the proposed method in SAR images despeckling.

scholarly journals Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anum Shafiq ◽  
Andaç Batur Çolak ◽  
Tabassum Naz Sindhu ◽  
Qasem M. Al-Mdallal ◽  
T. Abdeljawad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Generation ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Multi Layer Perceptron ◽  
Ann Model ◽  
Feed Forward ◽  
Williamson Fluid ◽  
Artificial Neural

AbstractIn current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.

scholarly journals IMPLEMENTASI ARTIFICIAL NEURAL NETWORK PADA FIELD PROGRAMMABLE GATE ARRAY (FPGA) DALAM SISTEM IDENTIFIKASI ODOR

Respati ◽  
2017 ◽  
Vol 10 (28) ◽  
Author(s):  
Dini Fakta Sari ◽  
Muhammad Rivai ◽  
Totok Mujiono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Field Programmable Gate Array ◽  
Back Propagation ◽  
Lookup Table ◽  
Multi Layer Perceptron ◽  
Field Programmable ◽  
Artificial Neural ◽  
Gate Array

ABSTRAKPenggunaan Field Programmable Gate Array (FPGA) untuk implementasi artificial neural network memberikan fleksibilitas dalam sistem pemrograman. Implementasi digital pada artificial neural network menggunakan FPGA dan menggunakan fungsi aktivasi nonlinier. VHDL digunakan untuk mengimplementasikan artificial neural network pada FPGA Xilinx XC3S500E-FG320 dengan perangkat lunak Xilinx ISE Webpack 8.2i. Kecepatan operasi FPGA Xilinx XC3S500E-FG320 dapat ditingkatkan dengan menggunakan metode lookup table (LUT). Jumlah LUT yang digunakan untuk perancangan artificial neural network dengan 3 neuron pada lapisan input, 4 neuron pada lapisan output dengan 1 neuron pada lapisan tersembunyi adalah sebesar 1407 LUT, untuk 5 neuron pada lapisan tersembunyi sebesar 4549 LUT, untuk 10 neuron pada lapisan tersembunyi sebesar 6378 LUT dan untuk 15 neuron pada lapisan tersembunyi sebesar 10084 LUT. Sistem dentifikasi odor, dilengkapi dengan sensor resonator kuarsa, pengkondisi sinyal, FPGA dan display. Model Multi Layer Perceptron (MLP) dengan metode pembelajaran Back Propagation (BP) yang digunakan untuk klasifikasi odor. Artificial neural network terdiri dari 3 neuron pada lapisan input, 10 neuron pada lapisan tersembunyi dan 4 neuron pada lapisan output yang diimplementasikan pada FPGA. Tingkat keberhasilan artificial neural network untuk identifikasi amoniak sebesar 93%, untuk pertamax sebesar 90%, untuk alkohol sebesar 92% dan untuk minyak tanah sebesar 85%.Kata kunci : Odor, sistem identifikasi odor, Artificial neural network, dan FPGA.

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