# A dense matching method for remote sensing images fused with CPS denoising

## TL;DR

This paper introduces a new method for matching remote sensing images that improves accuracy and speed by using CPS denoising.

## Contribution

The novel contribution is integrating CPS denoising with dense matching to enhance SGM algorithm performance in remote sensing.

## Key findings

- The proposed algorithm reduces the average mismatch rate by 13.1% compared to SGBM.
- The CPS denoising reduces the mismatch rate of reconstructed images by an average of 8.97%.
- The method increases the running speed by about 3 to 4 times compared to SGBM.

## 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 denoising. 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. The study conducted experimental validation and analysis of the mismatch rate, running time, and denoising effect of the algorithm using the Middlebury 2021 stereo datasets. Additionally, the matching results of the World-View3 satellite stereo image pairs were visualized and analyzed. The experimental results indicate that the proposed algorithm reduces the average mismatch rate by 13.1% and increases the running speed by about 3 to 4 times compared to the SGBM algorithm. Specifically, the denoising effect reduces the mismatch rate of the reconstructed image by an average of 8.97%. The results indicate that the CPS 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.