Title:	A BIG REMOTE SENSING DATA ANALYSIS USING DEEP LEARNING FRAMEWORK
Author(s):	Hanen Balti, Imen Chebbi,Nedra Mellouli, Imed Riadh Farah and Myriam Lamolle
ISBN:	978-989-8533-92-0
Editors:	Ajith P. Abraham and Jörg Roth
Year:	2019
Edition:	Single
Keywords:	Big Data, Remote Sensing, Deep Learning, Multi-label Classification, Feature extraction, Support Vector Machines
Type:	Full Paper
First Page:	119
Last Page:	126
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Spaceborne and airborne sensors deliver a huge number of Earth Observation Data every day. In this context, we can easily observe the whole earth from its different sides. Therefore, this big data is important in remote sensing and could be exploited in several domains requiring image classification, natural hazard monitoring, global climate change, agriculture, urban planning. Over the last five years, Convolutional Neural Networks (CNN) emerged as the most successful technique for the image classification task, as well as a number of other computer vision tasks. However, to train millions of parameters in CNN one requires a huge amount of annotated data. This requirement leads to a significant challenge if the available training data is limited for a target task at hand. To address this challenge, in the recent literature, researchers proposed various ways to apply a technique called Transfer Learning to transfer the knowledge gained by training CNNs parameters on some large annotated dataset to the target task with limited availability of training data. Most of our work in this paper was dedicated to proposing a hybrid classification of remote sensing images. This architecture combines Spark RDD image coding to consider image's local regions, pre-trained VGGNET-16 and UNET for image segmentation and SVM (Support Vector Machines) from spark Machine Learning to achieve labeling task.
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