期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 19, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2022.3171265
关键词
Feature extraction; Optical sensors; Optical imaging; Image matching; Adaptive optics; Nonlinear optics; Radar polarimetry; Image matching; masked structure features; synthetic aperture radar (SAR) and optical images
类别
资金
- National Natural Science Foundation of China [41401369]
This study proposes a robust matching method by using a multiscale masked structure feature representation. By extracting pixelwise gradient structure features on multiple scales of images and constructing a mask based on large contours, the proposed method significantly improves the matching performance.
Automatic and precise matching between optical and synthetic aperture radar (SAR) images is still a challenging task because of significant radiation and texture differences between such images. Recently, structure feature-based methods are popular for the matching of SAR and optical images. However, current structure descriptors include many noninformative features, which degrade their matching performance. To address that, we present a robust matching method by a multiscale masked structure feature representation. We first extract pixelwise gradient structure features on multiple scales of images. Then, a mask is constructed according to large contours of an image, which is used to increase the contribution of the main structure region and alleviate the influence of noninformative regions. Finally, a fast template scheme based on fast Fourier transform (FFT) is employed to obtain correspondences. The proposed method is tested using the optical and SAR images from the Sentinel and GaoFen sensors. Experiment results show that the proposed method significantly improves the matching performance compared with the state-of-the-art methods, especially for the images with poor structure features.
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