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Discussion on Related Key Technologies in Distributed Remote Sensing Image Processing

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DOI: 10.23977/jipta.2023.060107 | Downloads: 18 | Views: 568


Wu Qichen 1,2


1 No. 1 Geological Team of Shandong Provincial Bureau of Geology and Mineral Resources, Jinan, China
2 Key Laboratory of Cableway Intelligent Deformation Monitoring of Shandong Provincial Bureau of Geology & Mineral Resources, Jinan, China

Corresponding Author

Wu Qichen


Due to the rapid development of science and technology, remote sensing identification means have been applied in all aspects, especially in the civil and military, and have become an important means to obtain important information. Remote sensing image processing (processing of remote sensing image data) is a series of operations, such as radiation correction and geometric correction, image finishing, projection transformation, inlay, feature extraction, classification and various thematic processing, in order to achieve the desired purpose. Remote sensing image processing can be divided into two categories: one is to use optical, photographic and electronics methods to process remote sensing simulation images (photo, film), referred to as optical processing; the other is to use computer for a series of operations to obtain certain expected results, called remote sensing digital image processing. This paper will focus on the distributed remote sensing image processing method, and further study the possible challenges and key technologies in the practical application, in order to provide theoretical help for the practical work of workers in the same industry.


Distributed; remote sensing image processing; key technology


Wu Qichen, Discussion on Related Key Technologies in Distributed Remote Sensing Image Processing. Journal of Image Processing Theory and Applications (2023) Vol. 6: 67-72. DOI:


[1] Wang Yang, Zheng Qinbo, Zhang Junping. Target classification of multi-band data fusion using an evidence- theoretical approach [J]. Journal of Infrared and Millimeter Wave, 2016, (03): 71-74.
[2] Li Xiang, Yu Wenxian, Zhuang Zhaowen. Neural network model and algorithm for information fusion in the decision layer [J]. Journal of Electronics, 2017, (09): 117-120.
[3] Zhu Yahong, Wang Minle, Yang Xiande. An image feature correlation method based on region-invariant moments [J]. Modern Electronic Technology, 2018, (20): 75-78.
[4] Badar-Japan. Design of a hydrological feature analysis system based on remote sensing images [J]. Modern Electronic Technology, 2018, 41 (08): 68-71.
[5] Fu Hongyun. Near-view image line feature extraction and matching [D]. Beijing Institute of Civil Engineering and Architecture, 2012.
[6] Zhang Liyan, Pei Liang, Zhu Tianyi. An efficient algorithm for building linear feature extraction [J]. Surveying and spatial Geographic Information, 2017 (6): 136-138.

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