Review for Region Localization in Large-Scale Optical Remote Sensing Images

Shoulin Yin, Lin Teng

Source Title: International Journal of Imaging and Sensing Technologies and Applications (IJISTA)1(1)

ISSN: 2771-5647|EISSN: 2771-5655|EISBN13: 9781668445891|DOI: 10.4018/IJISTA.306654

MLA

Yin, Shoulin, and Lin Teng. "Review for Region Localization in Large-Scale Optical Remote Sensing Images." IJISTA vol.1, no.1 2022: pp.1-12. http://doi.org/10.4018/IJISTA.306654

APA

Yin, S. & Teng, L. (2022). Review for Region Localization in Large-Scale Optical Remote Sensing Images. International Journal of Imaging and Sensing Technologies and Applications (IJISTA), 1(1), 1-12. http://doi.org/10.4018/IJISTA.306654

Chicago

Yin, Shoulin, and Lin Teng. "Review for Region Localization in Large-Scale Optical Remote Sensing Images," International Journal of Imaging and Sensing Technologies and Applications (IJISTA) 1, no.1: 1-12. http://doi.org/10.4018/IJISTA.306654

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Abstract

For the massive large-scale visible image data obtained by satellite, unmanned aerial vehicles, and other reconnaissance platforms, if only relying on manual visual interpretation, there will be problems such as heavy workload, low efficiency, high repeatability, strong subjectivity, and high cost, which cannot meet the demand of modern society for efficient information. Therefore, in order to improve work efficiency, it is necessary to study the rapid automatic region localization in large-scale remote sensing images. That will play an important role in change detection, temperature retrieval, and other files. The development and present situation of the region localization algorithms are analyzed. This paper summarizes the development, improvement, and deficiency of the traditional algorithm, as well as the difficulties and challenges. And the authors make a comparison to the deep learning-based methods. Finally, a possible development direction is prospected.