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Proceedings Paper

A new two stages ATR architecture based on sparse auto encoder and learning network
Author(s): Mohamed Touafria; Qiang Yang
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Paper Abstract

This article discusses the issue of automatic target recognition (ATR) on Synthetic Aperture Radar images (SAR). Through learning the hierarchy of features automatically from massive training data, learning networks, such as Convolutional Neural Networks (CNN) has recently achieved the state-of-the-art results in many tasks. Moreover, unlike optical images, SAR imaging have the advantages of reduced sensitivity to weather conditions, day-night operation, penetration capability through obstacles, etc. Despite these utilities, several factors can affect the accuracy of the classification, such as errors linked with brightness values of the pixels and geometry registered by the satellite sensors. To correct these errors and extract better features about SAR targets, and obtain better accuracies a two steps algorithm called SAE-CNN-Recognizer(SCR) is proposed: Firstly, a pre-processing step consist of image enhancement is achieved using Sparse Auto-Encoder (SAE) to emphasize some image features for following analysis. Secondly, CNN architecture which consist of a feature extraction stage followed by a classification step using a softmax classifier. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove that this approach can accomplish an average accuracy higher than 97% on the classification of targets in ten categories, which is higher than the traditional CNN results.

Paper Details

Date Published: 9 August 2018
PDF: 7 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060J (9 August 2018); doi: 10.1117/12.2502944
Show Author Affiliations
Mohamed Touafria, Harbin Institute of Technology (China)
Qiang Yang, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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