A new algorithm of SAR target recognition based on advance deep learning neural network

نویسندگان

  • Hui Xu
  • Hong Gu
چکیده

In order to improve the accuracy of synthetic aperture radar images target recognition, we have proposed a new algorithm of SAR target recognition based on advance Deep Learning neural network. The traditional radar recognition algorithm has many disadvantages, In order to improve the accuracy of synthetic aperture radar images target recognition, the author have proposed a new algorithm of SAR target recognition based on advance Deep Learning neural network. In this paper, the author have got the feature of SAR image through the Refine Lee filter and HOG transformation firstly, and then realized the SAR object segmentation and recognition through the multi-layers RBM machine and GRNN neural network. and then realized the SAR object segmentation and recognition through the multi-layers RBM machine and GRNN neural network, and the learning rate parameter of the multilayers RBM machine is optimized through the GA algorithm. The simulation results shows that the object recognition rate of the algorithm proposed in this paper can reach 97%, which can improve the performance of the algorithm obviously.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Recognition of Sar Target Based on Multilayer Auto-encoder and Snn

Automatic target recognition (ATR) of synthetic aperture radar (SAR) image is investigated. One feature extraction algorithm of SAR image based on multilayer auto-encoder is proposed. The method makes use of a probabilistic neural network, restricted Boltzmann machine (RBM), modeling probability distribution of environment. Through the formation of more expressive multilayer neural network, the...

متن کامل

Modern Approaches in Deep Learning for SAR ATR

Recent breakthroughs in computational capabilities and optimization algorithms have enabled a new class of signal processing approaches based on deep neural networks (DNNs). These algorithms have been extremely successful in the classification of natural images, audio, and text data. In particular, a special type of DNNs, called convolutional neural networks (CNNs) have recently shown superior ...

متن کامل

Utilizing a new feed-back fuzzy neural network for solving a system of fuzzy equations

This paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. Our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. This architecture of articial neural networks, can get a real input vector and calculates its corresponding fuzzy o...

متن کامل

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes ...

متن کامل

A Hybrid Optimization Algorithm for Learning Deep Models

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014