Dense Image-Matching via Optical Flow Field Estimation and Fast-Guided Filter Refinement
| العنوان: | Dense Image-Matching via Optical Flow Field Estimation and Fast-Guided Filter Refinement |
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| المؤلفون: | Jianya Gong, Ryosuke Shibasaki, Shu Xu, Wei Yuan, Xiuxiao Yuan |
| المصدر: | Remote Sensing Volume 11 Issue 20 Pages: 2410 Remote Sensing, Vol 11, Iss 20, p 2410 (2019) |
| بيانات النشر: | MDPI AG, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Matching (statistics), Mean squared error, Computer science, Science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Optical flow, 02 engineering and technology, dense image-matching, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Pyramid (image processing), Aerial image, ComputingMethodologies_COMPUTERGRAPHICS, 021101 geological & geomatics engineering, aerial image, Pixel, business.industry, Ground sample distance, Filter (signal processing), matching success rate, optical flow field, fast guided filtering, General Earth and Planetary Sciences, 020201 artificial intelligence & image processing, Artificial intelligence, business |
| الوصف: | The development of an efficient and robust method for dense image-matching has been a technical challenge due to high variations in illumination and ground features of aerial images of large areas. In this paper, we propose a method for the dense matching of aerial images using an optical flow field and a fast-guided filter. The proposed method utilizes a coarse-to-fine matching strategy for a pixel-wise correspondence search across stereo image pairs. The pyramid Lucas–Kanade (L–K) method is first used to generate a sparse optical flow field within the stereo image pairs, and an adjusted control lattice is then used to derive the multi-level B-spline interpolating function for estimating the dense optical flow field. The dense correspondence is subsequently refined through a combination of a novel cross-region-based voting process and fast guided filtering. The performance of the proposed method was evaluated on three bases, namely, the matching accuracy, the matching success rate, and the matching efficiency. The evaluative experiments were performed using sets of unmanned aerial vehicle (UAV) images and aerial digital mapping camera (DMC) images. The results showed that the proposed method afforded the root mean square error (RMSE) of the reprojection errors better than ±0.5 pixels in image, and a height accuracy within ±2.5 GSD (ground sampling distance) from the ground. The method was further compared with the state-of-the-art commercial software SURE and confirmed to deliver more complete matches for images with poor-texture areas, the matching success rate of the proposed method is higher than 97% while SURE is 96%, and there is 47% higher matching efficiency. This demonstrates the superior applicability of the proposed method to aerial image-based dense matching with poor texture regions. |
| وصف الملف: | application/pdf |
| تدمد: | 2072-4292 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd4ef34f092e01ed54c9adfa428e5dd9 https://doi.org/10.3390/rs11202410 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....dd4ef34f092e01ed54c9adfa428e5dd9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20724292 |
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