MF‐SarNet: Effective CNN with data augmentation for SAR automatic target recognition

Zhai, Yongjie; Ma, Hui; Cao, He; Deng, Wenbo; Liu, Jian; Zhang, Zhongyi; Guan, Huixin; Zhi, Yihang; Wang, Jinxing; Zhou, Jihua

doi:10.1049/joe.2019.0218

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The CNNs represent an efficient DL structure [88], which can learn highly abstract spatial features from the original representation of an image through a series of convolutional, pooling, and fully-connected operations and activation by nonlinear functions [95]. In each layer, the convolution operation extracts features from an input image and constructs a feature map [96].…”

Section: Algorithmsmentioning

confidence: 99%

Monitoring of Soil Salinization in the Keriya Oasis Based on Deep Learning with PALSAR-2 and Landsat-8 Datasets

et al. 2022

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Currently, soil salinization is one of the main forms of land degradation and desertification. Soil salinization not only seriously restricts the development of agriculture and the economy, but also poses a threat to the ecological environment. The main purpose of this study is to map soil salinity in Keriya Oasis, northwestern China using the PALSAR-2 fully polarized synthetic aperture radar (PolSAR) L-band data and Landsat8-OLI (OLI) optical data combined with deep learning (DL) methods. A field survey is conducted, and soil samples are collected from 20 April 2015 to 1 May 2015. To mine the hidden information in the PALSAR-2 data, multiple polarimetric decomposition methods are implemented, and a wide range of polarimetric parameters and synthetic aperture radar discriminators are derived. The radar vegetation index (RVI) is calculated using PALSAR-2 data, while the normalized difference vegetation index (NDVI) and salinity index (SI) are calculated using OLI data. The random forest (RF)-integrated learning algorithm is used to select the optimal feature subset composed of eight polarimetric elements. The RF, support vector machine, and DL methods are used to extract different degrees of salinized soil. The results show that the OLI+PALSAR-2 image classification result of the DL classification was relatively good, having the highest overall accuracy of 91.86% and a kappa coefficient of 0.90. This method is helpful to understand and monitor the spatial distribution of soil salinity more effectively to achieve sustainable agricultural development and ecological stability.

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Section: Algorithmsmentioning

confidence: 99%

Monitoring of Soil Salinization in the Keriya Oasis Based on Deep Learning with PALSAR-2 and Landsat-8 Datasets

et al. 2022

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“…The CNN is high performance in-depth learning network used for image processing. It conducts feature extraction on the original images through several convolution layers, a pooling layer, and activation functions, and then substitutes the obtained feature map into the fully connected layer for the back-propagation algorithm [44][45][46][47][48]. A convolutional neural network is composed of an input layer, a convolution layer, a pooling layer, an activation layer, a fully connected layer, and an output layer [49].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components

Jian

Yau

2021

IEEE Access

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Ball bearings are one of the most common components used in rotating machines. They reduce the rotational friction between the shaft and fixed components and maintain the center line of rotation of the shaft. A damaged bearing will cause abnormal vibration and noise, and often results in machine failure and loss of production. In this study the public database on ball bearings, provided by the Vibration Institute of Machinery Failure Prevention Technology (MFPT), was used for data retrieval and analysis, and a diagnosis model was created according to the data sets of the bearing in the database. Three different approaches were used for the extraction of features and a classifier was used to implement a diagnostic system. The aim of this study was a comparison of three approaches. The first was the Short-time Fourier Transform (STFT) where the time-frequency domain image is extracted as the feature used for status identification. The second and third approaches were based on the Chen-Lee Chaotic and the Lorenz Chaotic Systems and chaotic dynamic error maps were used in analysis and feature status identification. Chaotic systems are particularly sensitive to the slightest changes in input signals, and the time domain signals from bearings in different conditions were mapped onto individual images. The feature images extracted by the three different approaches were then used for training and verification in a Convolutional Neural Network (CNN). From the results of the experiments, it can be seen that all three approaches gave high identification rates. The interactive verification identification rate of the Chen-Lee chaotic system with CNN under three statuses reached 98.33%, and it also had the best computational efficiency in the condition without losing any classification accuracy. This will make a substantial contribution to real-time ball bearing fault diagnosis.

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“…A novel neighborhood preserved DNN (NPDNN) was proposed in [350] to exploit the spatial relation between pixels by a jointly weighting strategy for PolSAR image classification. An convolution kernel of the fire module based effective max-fire CNN model, called MF-SarNet, was constructed in [351] for effective SAR-ATR tasks.…”

Section: A Sar Images Processingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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MF‐SarNet: Effective CNN with data augmentation for SAR automatic target recognition

Cited by 5 publications

References 13 publications

Monitoring of Soil Salinization in the Keriya Oasis Based on Deep Learning with PALSAR-2 and Landsat-8 Datasets

Monitoring of Soil Salinization in the Keriya Oasis Based on Deep Learning with PALSAR-2 and Landsat-8 Datasets

Bearing Fault Diagnosis Based on Chaotic Dynamic Errors in Key Components

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

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