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Automatic mosaic method of remote sensing images based on machine vision
S.P. Gao 1, M. Xia 1,2, S.J. Zhang 3

1 School of Mechanical and Electrical Engineering, Quanzhou University of Information Engineering,
362000, China, Quanzhou, Fengze District, Bodong Road, No. 249;
2 Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology,
210044, China, Nanjing, Ningliu Road, No.219;
3 College of Engineering and Technology, Yang-En University,
362014, China, Quanzhou, Luojiang District, Majia Town

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DOI: 10.18287/2412-6179-CO-1383

Страницы: 705-713.

Язык статьи: English.

Аннотация:
Unmanned Aerial Vehicle (UAV) remote sensing is a commonly used technical means in modern science and technology, but currently, remote sensing images captured by UAVs need to be spliced to obtain more comprehensive information. However, current image stitching techniques generally have shortcomings such as a small number of extracted features, low matching accuracy, and poor stability. To address the above issues, this study proposes an improved remote sensing image mosaic model on the bias of the Scale Invariant Feature Transform (SIFT) algorithm. Firstly, in this study, aiming at the problem that traditional SIFT cannot meet the requirements of feature extraction and matching for unconventional remote sensing images and special texture images, normalized cross correlation (NCC) and Forstner operator are introduced to optimize it, namely, a SIFT-NCC model is constructed. On this basis, for remote sensing images with high resolution and a wide range, this study further proposes a remote sensing image automatic mosaic model that combines point features and line features. That is, a linear segment detector (LSD) is introduced to extract the line features of remote sensing images. The experimental verification results of the final SIFT-NCC-LSD show that the matching accuracy for remote sensing images with different characteristics can reach over 95 %. Therefore, SIFT-NCC-LSD has good applicability.

Ключевые слова:
machine vision; remote sensing images; automatic splicing; point characteristics; line features; SIFT.

Благодарности
The research is supported by: Educational Teaching Reform of Undergraduate Universities in Fujian Province-Reform of Practical Teaching Mode for Cultivating Local Applied Undergraduate Engineering Talents under the Background of Integration of Industry and Education (No.: FBJG20200355).

Citation:
Gao SP, Xia M, Zhang SJ. Automatic mosaic method of remote sensing images based on machine vision. Computer Optics 2024; 48(5): 705-713. DOI: 10.18287/2412-6179-CO-1383.

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