Affiliation:
1. Naval University of Engineering
Abstract
Abstract
Dense matching of remote sensing images is crucial for 3D reconstruction. This study proposes an enhanced dense matching method employing the CPS image denoising algorithm, aiming to boost the SGM algorithm's accuracy and efficiency in remote sensing image matching. The stereo image pair's quality is evaluated using the PSNR index, and a decision-making criterion based on the CPS algorithm is incorporated to determine the need for noise reduction. Preprocessing steps, including image cropping and pixel coordinate transformation, significantly reduce computational requirements. An epipolar line model, minimizing the disparity between two pixels, is used for calculations. This model is employed to construct an epipolar image, enhancing the accuracy and efficiency of the process. Experimental validation and analysis confirm that this method effectively addresses dense matching challenges in the presence of image blur and noise, thereby improving the operational efficiency and accuracy of the dense matching algorithm.
Publisher
Research Square Platform LLC
Reference19 articles.
1. A review of developments in the theory and technology of three-dimensional reconstruction in digital aerial photogrammetry[J];Li ZHANG;Acta Geodaetica et Cartographica Sinica,2022
2. Liming Chen. A Fast Trilateral Filter-Based Adaptive Support Weight Method for Stereo Matching;Chen Dongming;IEEE Trans. Circuits Syst. Video Technol.,2015
3. Yao, P. et al. Segment-tree based cost aggregation for stereo matching with enhanced segmentation advantage. in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2027–2031 (IEEE, 2017). doi:10.1109/ICASSP.2017.7952512.
4. Geiger, A., Roser, M. & Urtasun, R. Efficient Large-Scale Stereo Matching. in Computer Vision – ACCV 2010 (eds. Kimmel, R., Klette, R. & Sugimoto, A.) vol. 6492 25–38 (Springer Berlin Heidelberg, 2011).
5. Hirschmuller, H. Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information. in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) vol. 2 807–814 (IEEE, 2005).