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International Journal of Educational Innovation and Science, 2022, 3(4); doi: 10.38007/IJEIS.2022.030403.

Remote Sensing Image Classification Model Based on Multimedia Network and Its Knowledge Integration Method


Nasser Jaber

Corresponding Author:
Nasser Jaber

Universiti Teknologi MARA, Malaysia


In recent years, with the rapid development of remote sensing satellite technology, the resolution of images generated by remote sensing satellites has been increasing. Today, high-resolution remote sensing images (referred to as high-resolution remote sensing images) have become the focus of research in the field of remote sensing. The feature information of high-resolution remote sensing images is richer and more accurate than the medium-low resolution remote sensing image information, and it has more practical significance in practical applications: remote sensing application workers and research scholars perform feature extraction and scenes on high-resolution remote sensing images. Analysis and research on classification, feature recognition, and target detection to meet the actual needs of high-resolution remote sensing images in the military and economic fields as well as in the civil and civilian fields. Among them, feature extraction is the basis for understanding and analyzing high-resolution remote sensing images. Through various feature extraction techniques, the information of the essential attributes in the high-resolution remote sensing images is extracted as features, and the features are used to describe the high-resolution remote sensing images, which facilitates accurate and reasonable understanding and analysis of high-resolution remote sensing images, and avoids redundancy. The negative impacts of information, noise, etc. in practical applications, and thus more efficient and accurate completion of the classification, identification and detection of high-resolution remote sensing images. This paper mainly discusses the research progress of deep network model of high-resolution remote sensing image based on the deep network model of multimedia-based Convolutional Neural Network (CNN), and through the experimental results of high-resolution remote sensing image specific problems, Performance comparison and evaluation based on features extracted by the CNN-based deep network model. Finally, the problems of CNS-based deep network model in the extraction of deep semantic features in high-resolution remote sensing images and future development trends are analyzed, which provides reference for the feature classification tasks of high-resolution remote sensing images.


Multimedia, Remote Sensing Image, Convolutional Nerve, CNN

Cite This Paper

Nasser Jaber. Remote Sensing Image Classification Model Based on Multimedia Network and Its Knowledge Integration Method. International Journal of Educational Innovation and Science (2022), Vol. 3, Issue 4: 26-41. https://doi.org/10.38007/IJEIS.2022.030403.


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