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Improved WαSH Feature Matching Based on 2D-DWT for Stereo Remote Sensing Images
Mei Yu1,2,3, Kazhong Deng4,5, Huachao Yang6,7
1NASG Key Laboratory of Land and Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China. tb15160011b2@cumt.edu.cn.
Sensors (Basel, Switzerland)
|October 19, 2018
Summary
This study enhances Weighted α-shape (WαSH) feature matching for stereo remote sensing images by integrating 2D discrete wavelet transform (2D-DWT). The improved methods, particularly IWWF, significantly increase feature matches and stability, outperforming existing techniques in challenging conditions.
Area of Science:
- Computer Vision
- Remote Sensing
- Image Processing
Background:
- Geometric and radiometric distortions pose challenges for stereo remote sensing image matching.
- Weighted α-shape (WαSH) features offer tolerance to distortions but suffer from low feature counts and noise sensitivity.
- Existing WαSH methods struggle to achieve sufficient matches for accurate geometric matrix estimation.
Purpose of the Study:
- To improve the robustness and accuracy of WαSH feature matching in stereo remote sensing images.
- To address the limitations of WαSH detectors, including low feature detection and noise sensitivity.
- To introduce novel WαSH feature matching methods leveraging 2D discrete wavelet transform (2D-DWT).
Main Methods:
- Implementation of 2D-DWT on images to create transformed representations.
- Detection of WαSH features on the 2D-DWT transformed images.
- Development of three distinct matching strategies: WWF, IWWF, and LIWWF, based on sub-image characteristics and descriptor construction.
Main Results:
- The proposed methods (WWF, IWWF, LIWWF) generated more matches and exhibited greater stability compared to the original WαSH.
- Under affine distortion, scale distortion, illumination change, and noise, the methods achieved a correct matching rate exceeding 90% on certain datasets.
- IWWF demonstrated superior performance, achieving a 71.42% correct matching rate for severely distorted images where KAZE failed (35.71%) and maintaining over 50% for all tested stereo pairs.
Conclusions:
- Integrating 2D-DWT with WαSH features significantly enhances matching performance for stereo remote sensing images.
- The improved wavelet transform WαSH features (IWWF) method offers a stable and accurate solution for image matching, even with severe distortions.
- The proposed methods provide a more reliable alternative to existing techniques for essential tasks like homography matrix estimation in remote sensing applications.
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