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Register a new userSentinel-1 Additive Noise Removal from Cross-Polarization Extra-Wide TOPSAR with Dynamic Least-Squares
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Added by
Peter Q. Lee
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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Sentinel-1 is a synthetic aperture radar (SAR) platform with an operational mode called extra wide (EW) that offers large regions of ocean areas to be observed. A major issue with EW images is that the cross-polarized HV and VH channels have prominent additive noise patterns relative to low backscatter intensity, which disrupts tasks that require manual or automated interpretation. The European Space Agency (ESA) provides a method for removing the additive noise pattern by means of lookup tables, but applying them directly produces unsatisfactory results because characteristics of the noise still remain. Furthermore, evidence suggests that the magnitude of the additive noise dynamically depends on factors that are not considered by the ESA estimated noise field. To address these issues we propose a quadratic objective function to model the mis-scale of the provided noise field on an image. We consider a linear denoising model that re-scales the noise field for each subswath, whose parameters are found from a least-squares solution over the objective function. This method greatly reduces the presence of additive noise while not requiring a set of training images, is robust to heterogeneity in images, dynamically estimates parameters for each image, and finds parameters using a closed-form solution. Two experiments were performed to validate the proposed method. The first experiment simulated noise removal on a set of RADARSAT-2 images with noise fields artificially imposed on them. The second experiment conducted noise removal on a set of Sentinel-1 images taken over the five oceans. Afterwards, quality of the noise removal was evaluated based on the appearance of open-water. The two experiments indicate that the proposed method marks an improvement both visually and through numerical measures.
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Co-registering the Sentinel-1 SAR and Sentinel-2 optical data of European Space Agency (ESA) is of great importance for many remote sensing applications. However, we find that there are evident misregistration shifts between the Sentinel-1 SAR and Sentinel-2 optical images that are directly downloaded from the official website. To address that, this paper presents a fast and effective registration method for the two types of images. In the proposed method, a block-based scheme is first designed to extract evenly distributed interest points. Then the correspondences are detected by using the similarity of structural features between the SAR and optical images, where the three dimension (3D) phase correlation (PC) is used as the similarity measure for accelerating image matching. Finally, the obtained correspondences are employed to measure the misregistration shifts between the images. Moreover, to eliminate the misregistration, we use some representative geometric transformation models such as polynomial models, projective models, and rational function models for the co-registration of the two types of images, and compare and analyze their registration accuracy under different numbers of control points and different terrains. Six pairs of the Sentinel-1 SAR L1 and Sentinel-2 optical L1C images covering three different terrains are tested in our experiments. Experimental results show that the proposed method can achieve precise correspondences between the images, and the 3rd. Order polynomial achieves the most satisfactory registration results. Its registration accuracy of the flat areas is less than 1.0 10m pixels, and that of the hilly areas is about 1.5 10m pixels, and that of the mountainous areas is between 1.7 and 2.3 10m pixels, which significantly improves the co-registration accuracy of the Sentinel-1 SAR and Sentinel-2 optical images.
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