Deep Semantic Feature Detection from Multispectral Satellite Images

Hanen Balti, Nedra Mellouli, Imen Chebbi, Imed Farah, Myriam Lamolle


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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