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Authors: Hanen Balti 1 ; Nedra Mellouli 2 ; Imen Chebbi 2 ; Imed Farah 1 and Myriam Lamolle 2

Affiliations: 1 RIADI Laboratory, University of Manouba, Manouba and Tunisia ; 2 LIASD Laboratory, University of Paris 8, Paris and France

ISBN: 978-989-758-382-7

Keyword(s): Big Data, Remote Sensing, Feature Detection, CNN, Semantic Segmentation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Recent progress in satellite technology has resulted in explosive growth in volume and quality of high-resolution remote sensing images. To solve the issues of retrieving high-resolution remote sensing (RS) data in both efficiency and precision, this paper proposes a distributed system architecture for object detection in satellite images using a fully connected neural network. On the one hand, to address the issue of higher computational complexity and storage ability, the Hadoop framework is used to handle satellite image data using parallel architecture. On the other hand, deep semantic features are extracted using Convolutional Neural Network (CNN),in order to identify objects and accurately locate them. Experiments are held out on several datasets to analyze the efficiency of the suggested distributed system. Experimental results indicate that our system architecture is simple and sustainable, both efficiency and precision can satisfy realistic requirements.

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Paper citation in several formats:
Balti, H.; Mellouli, N.; Chebbi, I.; Farah, I. and Lamolle, M. (2019). Deep Semantic Feature Detection from Multispectral Satellite Images.In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-382-7, pages 458-466. DOI: 10.5220/0008350004580466

@conference{kdir19,
author={Hanen Balti. and Nedra Mellouli. and Imen Chebbi. and Imed Riadh Farah. and Myriam Lamolle.},
title={Deep Semantic Feature Detection from Multispectral Satellite Images},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2019},
pages={458-466},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008350004580466},
isbn={978-989-758-382-7},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Deep Semantic Feature Detection from Multispectral Satellite Images
SN - 978-989-758-382-7
AU - Balti, H.
AU - Mellouli, N.
AU - Chebbi, I.
AU - Farah, I.
AU - Lamolle, M.
PY - 2019
SP - 458
EP - 466
DO - 10.5220/0008350004580466

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