Efficient scene analysis by a deep learning-long short-term memory approach based on polarimetric measurements

ABSTRACT

Polarimetric imaging has been considered one of the crucial research fields in tactical object recognition in the scene. The present paper addresses the detection and classification of different objects in the scene based on data of polarization cameras. Initially, a pre-treatment phase is conducted where the angle and the degree of polarization of each region of the scene are obtained by polarimetric images. After that stage, stokes-based parameters are extracted from the polarimetric images. In addition, Mueller matrix decomposition is also applied to extract attributes. In the classification step, we applied several machine-learning algorithms. These classification methods are mainly chosen due to their (i) high generalization performance with nonlinear data and (ii) powerfulness in big data separation. Finally, we applied these strategies to the publicly available PolaBot database from Bourgogne Franche-Comte University. The obtained results indicated that the proposed strategy accurately detected and differentiated between different classes in the scene.

KEYWORDS:

• Deep learning
• object recognition
• machine learning
• polarimetric imagery
• image demosaicking
• division-of-focal-plane (DoFP)
• LSTM classification
• stokes vector
• Mueller matrix decomposition

Acknowledgements

The authors would like to thank the Center for Development of Advanced Technologies (CDTA) for its continued support during the research Authors’ contributions: Radhwane Boudaoud: experimental work, data processing, software, investigation, reviewing and editing; Abdelkrim Kedadra: supervision, methodology, review and editing; Zerrouki Nabil: methodology, analysing the obtained results, data and validation, writing the article; Abdelkader Aissat: supervision, methodology, review and editing.

Statements and declarations: ethical approval

This study complies with ethical standards. All the authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Additional information

Funding

No funding was received for conducting this study.

Notes on contributors

Radhwane Boudaoud

Radhwane Boudaoud received a Master’s degree in telecommunication from the Abou Bakr Belkaïd University of Tlemcen, Algeria, in 2011. He is currently Scientist with the Center for Development of Advanced Technologies, Algiers, Algeria. His main research interests include image processing, pattern recognition, and polarimetric imagery.

Abdelkrim Kedadra

Abdelkrim Kedadra received a Ph.D. degree in electrical engineering from the Ecole Centrale de Lyon, France, in 1996. At CDTA, he has worked on lidar development for environmental applications. He is currently the head of a research team at the Center for Development of Advanced Technologies, Algiers, Algeria. His main research interests include optoelectronic, image processing, and polarimetric imagery.

Nabil Zerrouki

Nabil Zerrouki received a Ph.D. degree in signal and image processing from the University of Sciences and Technology Houari Boumedienne (USTHB), Algiers, Algeria, in 2018. He is currently Scientist with the Center for Development of Advanced Technologies, Algiers, Algeria. His main research interests include image processing, machine learning, pattern recognition, and remote sensing.

Abdelkader Aissat

Abdelkader Aissat received the magister diploma and doctorate in electronics from the University of Blida, ENP Algeria in 1999 and 2007, respectively. He became a lecturer in the department of electronics at Blida University. He was the head of the electronics department. He worked in the Engineering Faculty of Blida University from 2000 to –2003 as the director of pedagogy and research. His research interests include materials, semiconductors, semiconductors lasers, detection, and modelling of the components optoelectronic, microwaves, and mixed. He is an active reviewer in Elsevier, IEEE, and Springer. He has several publications and conferences (232). He has achieved international projects in the field of materials, new materials, and nanostructures for optoelectronics and photovoltaics.