Deep learning-based explainable target classification for synthetic aperture radar images

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Conference article in proceedings
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Date
2020-06
Department
Thapar Institute of Engineering and Technology
Department of Computer Science
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Mcode
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Language
en
Pages
6
34-39
Series
2020 13TH INTERNATIONAL CONFERENCE ON HUMAN SYSTEM INTERACTION (HSI), Conference on Human System Interaction
Abstract
Deep learning has been extensively useful for its ability to mimic the human brain to make decisions. It is able to extract features automatically and train the model for classification and regression problems involved with complex images databases. This paper presents the image classification using Convolutional Neural Network (CNN) for target recognition using Synthetic-aperture Radar (SAR) database along with Explainable Artificial Intelligence (XAI) to justify the obtained results. In this work, we experimented with various CNN architectures on the MSTAR dataset, which is a special type of SAR images. Accuracy of target classification is almost 98.78% for the underlying preprocessed MSTAR database with given parameter options in CNN. XAI has been incorporated to explain the justification of test images by marking the decision boundary to reason the region of interest. Thus XAI based image classification is a robust prototype for automatic and transparent learning system while reducing the semantic gap between soft-computing and humans way of perception.
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Keywords
Artificial intelligence, deep learning, image classification, target recognition, synthetic aperture radar, SAR TARGET
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DOI
10.1109/HSI49210.2020.9142658
Citation
Mandeep , Pannu , H S & Malhi , A 2020 , Deep learning-based explainable target classification for synthetic aperture radar images . in Proceedings - 2020 13th International Conference on Human System Interaction, HSI 2020 . , 9142658 , Conference on Human System Interaction , IEEE , pp. 34-39 , International Conference on Human System Interaction , Tokyo , Japan , 06/06/2020 . https://doi.org/10.1109/HSI49210.2020.9142658
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https://urn.fi/URN:NBN:fi:aalto-202009185453
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[article-cris] Perustieteiden korkeakoulu / SCI