Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas
The performance of Persistent Scatterer Interferometry (PSI) depends heavily on Persistent Scatterer (PS) density. In order to increase PS density, we can apply Super-Resolution reprocessing algorithms in PSI. Involving the reprocessing algorithms and the peak-detection-based Persistent Scatterer Ca...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9211411/ |
| _version_ | 1811274110274109440 |
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| author | Hao Zhang Paco Lopez-Dekker Shaoning Li |
| author_facet | Hao Zhang Paco Lopez-Dekker Shaoning Li |
| author_sort | Hao Zhang |
| collection | DOAJ |
| description | The performance of Persistent Scatterer Interferometry (PSI) depends heavily on Persistent Scatterer (PS) density. In order to increase PS density, we can apply Super-Resolution reprocessing algorithms in PSI. Involving the reprocessing algorithms and the peak-detection-based Persistent Scatterer Candidate points (PSCs) selection method, the full PSI chain is referred to as Super-Resolution PSI (SR-PSI). The implementation of the Super-Resolution reprocessing algorithm, however, is computationally intensive, which makes SR-PSI time-consuming. In this work, we propose to improve the efficiency by constraining the Capon-based reprocessing to the non-homogeneous areas (e.g., urban areas). We notice that the Capon algorithm performs similarly as the Fourier-based algorithm for homogeneous regions (e.g., grassland), thus we can use Single Look Complex (SLC) images for these areas. With the Coefficient of Variation (CV) as the index, we divide the full image into two classes: homogeneous areas, for which we select PSCs from the original stack, and non-homogeneous areas, for which we extract PSCs from the Capon-based reprocessed images. Then we combine the PSCs of both cases for further PSI processing. We applied the combination method to a stack of TerraSAR-X data. The results show that the proposed approach is more computationally efficient than the original SR-PSI with the effectiveness uncompromised, especially for applications aiming at the urban deformation. |
| first_indexed | 2024-04-12T23:13:09Z |
| format | Article |
| id | doaj.art-c2b4cba9d0c7448ca8af5cadfd6b2e5a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:13:09Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c2b4cba9d0c7448ca8af5cadfd6b2e5a2022-12-22T03:12:46ZengIEEEIEEE Access2169-35362020-01-01818164018164910.1109/ACCESS.2020.30284919211411Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous AreasHao Zhang0https://orcid.org/0000-0001-7089-8344Paco Lopez-Dekker1https://orcid.org/0000-0002-0221-4403Shaoning Li2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaDepartment of Geoscience and Remote Sensing, Delft University of Technology, Delft, The NetherlandsNational-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan, ChinaThe performance of Persistent Scatterer Interferometry (PSI) depends heavily on Persistent Scatterer (PS) density. In order to increase PS density, we can apply Super-Resolution reprocessing algorithms in PSI. Involving the reprocessing algorithms and the peak-detection-based Persistent Scatterer Candidate points (PSCs) selection method, the full PSI chain is referred to as Super-Resolution PSI (SR-PSI). The implementation of the Super-Resolution reprocessing algorithm, however, is computationally intensive, which makes SR-PSI time-consuming. In this work, we propose to improve the efficiency by constraining the Capon-based reprocessing to the non-homogeneous areas (e.g., urban areas). We notice that the Capon algorithm performs similarly as the Fourier-based algorithm for homogeneous regions (e.g., grassland), thus we can use Single Look Complex (SLC) images for these areas. With the Coefficient of Variation (CV) as the index, we divide the full image into two classes: homogeneous areas, for which we select PSCs from the original stack, and non-homogeneous areas, for which we extract PSCs from the Capon-based reprocessed images. Then we combine the PSCs of both cases for further PSI processing. We applied the combination method to a stack of TerraSAR-X data. The results show that the proposed approach is more computationally efficient than the original SR-PSI with the effectiveness uncompromised, especially for applications aiming at the urban deformation.https://ieeexplore.ieee.org/document/9211411/Homogeneous areasuper-resolutionSARPSI |
| spellingShingle | Hao Zhang Paco Lopez-Dekker Shaoning Li Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas IEEE Access Homogeneous area super-resolution SAR PSI |
| title | Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas |
| title_full | Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas |
| title_fullStr | Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas |
| title_full_unstemmed | Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas |
| title_short | Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas |
| title_sort | combination of super resolution psi and traditional psi by identification of homogeneous areas |
| topic | Homogeneous area super-resolution SAR PSI |
| url | https://ieeexplore.ieee.org/document/9211411/ |
| work_keys_str_mv | AT haozhang combinationofsuperresolutionpsiandtraditionalpsibyidentificationofhomogeneousareas AT pacolopezdekker combinationofsuperresolutionpsiandtraditionalpsibyidentificationofhomogeneousareas AT shaoningli combinationofsuperresolutionpsiandtraditionalpsibyidentificationofhomogeneousareas |