loading
Papers

Research.Publish.Connect.

Paper

Authors: Imen Chebbi 1 ; Nedra Mellouli 2 ; Myriam lamolle 2 and Imed Farah 3

Affiliations: 1 LIASD Laboratory, University of Paris 8, Paris, France, RIADI Laboratory, University Of Manouba, Manouba and Tunisia ; 2 LIASD Laboratory, University of Paris 8, Paris and France ; 3 RIADI Laboratory, University Of Manouba, Manouba and Tunisia

ISBN: 978-989-758-382-7

Keyword(s): Big Data, Deep Learning, Remote Sensing, Classification, Spark, Tensorflow.

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

Abstract: Large data remote sensing has various special characteristics, including multi-source, multi-scale, large scale, dynamic and non-linear characteristics. Data set collections are so large and complex that it becomes difficult to process them using available database management tools or traditional data processing applications. In addition, traditional data processing techniques have different limitations in processing massive volumes of data, as the analysis of large data requires sophisticated algorithms based on machine learning and deep learning techniques to process the data in real time with great accuracy and efficiency. Therefore Deep learning methods are used in various domains such as speech recognition, image classifications, and learning methods in language processing. However, recent researches merged different deep learning techniques with hybrid learning-training mechanisms and processing data with high speed. In this paper we propose a hybrid approach for RS image classi fication combining a deep learning algorithm and an explanatory classification algorithm. We show how deep learning techniques can benefit to Big remote sensing. Through deep learning we seek to extract relevant features from images via a DL architecture. Then these characteristics are the entry points for the MLlib classification algorithm to understand the correlations that may exist between characteristics and classes. This architecture combines Spark RDD image coding to consider image’s local regions, pre-trained Vggnet and U-net for image segmentation and spark Machine Learning like random Forest and KNN to achieve labeling task. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.237.51.35

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Chebbi, I.; Mellouli, N.; lamolle, M. and Farah, I. (2019). Deep Learning Analysis for Big Remote Sensing Image Classification.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 355-362. DOI: 10.5220/0008166303550362

@conference{kdir19,
author={Imen Chebbi. and Nedra Mellouli. and Myriam lamolle. and Imed Riadh Farah.},
title={Deep Learning Analysis for Big Remote Sensing Image Classification},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2019},
pages={355-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008166303550362},
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 Learning Analysis for Big Remote Sensing Image Classification
SN - 978-989-758-382-7
AU - Chebbi, I.
AU - Mellouli, N.
AU - lamolle, M.
AU - Farah, I.
PY - 2019
SP - 355
EP - 362
DO - 10.5220/0008166303550362

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.