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

Mandeep*, Husanbir Singh Pannu, Avleen Malhi

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)
133 Lataukset (Pure)


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.

OtsikkoProceedings - 2020 13th International Conference on Human System Interaction, HSI 2020
KustantajaIEEE Computer Society
ISBN (elektroninen)9781728173924
DOI - pysyväislinkit
TilaJulkaistu - kesäkuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Human System Interaction - Tokyo, Japani
Kesto: 6 kesäkuuta 20208 kesäkuuta 2020
Konferenssinumero: 13


NimiConference on Human System Interaction
ISSN (painettu)2158-2246
ISSN (elektroninen)2158-2254


ConferenceInternational Conference on Human System Interaction

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