Deep learning technique for image satellite processing

DOI: https://doi.org/10.58190/imiens.2023.9

Keywords

Convolutional neural network, Satellite Images, Protocol, Remote Sensing

Abstract

Recently, several technological solutions based on deep learning have been developed for the processing of multispectral satellite images. These solutions have broadened the scope of spatial remote sensing and further explored the earth and space by avoiding human efforts in heavy manual tasks. However, even though this progress gives even better performance it is difficult to design an efficient deep-learning pipeline for satellite image processing in the absence of a well-developed standard. Thus, we propose a new framework to design and implement a machine-learning model for satellite image processing through a set of machine-learning methods in the form of a protocol adapted to the context of space imagery. The choice of the dataset adapted metrics, exploitation of the product format, data enrichment, spatiotemporal indexing and analysis, contrast enhancement and noise reduction/suppression, network parameterization, model scalability, data normalization, spectral dependency, and processing complexity reduction are among the methods adapted to the processing of multispectral satellite images by deep learning. All these methods are analyzed in depth in order to understand their usefulness and their contribution to the performance of learning models before testing them on real use cases. This method constitutes the first standard framework for the use of deep learning in the processing of multispectral satellite images.

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    • Published: 2023-03-21

    Issue: Vol. 2 No. 1 (2023) (view)

    Section: Research Articles

    How to cite:
    [1]
    O. Soufi and F.- zahra Belouadha, “Deep learning technique for image satellite processing”, Intell Methods Eng Sci, vol. 2, no. 1, pp. 27–34, Mar. 2023.

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