Skip to main content

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

Mandeep, H..P. and Malhi, A., 2020. Deep learning-based explainable target classification for synthetic aperture radar images. In: HSI 2020: 13th International Conference on Human System Interaction, 6-8 June 2020, Tokyo, Japan.

Full text available as:

[img]
Preview
PDF
SCI_Mandeep_Deep_Learning_Based.XAI (1).pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.

2MB

DOI: 10.1109/HSI49210.2020.9142658

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.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Artificial intelligence; deep learning; image classification; target recognition; synthetic aperture radar
Group:Faculty of Science & Technology
ID Code:36360
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:13 Dec 2021 10:51
Last Modified:13 Dec 2021 10:51

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -