Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data
Distributed scatterers (DSs) are necessary to increase point density in multi-temporal InSAR (MT-InSAR) monitoring. The identification of homogeneous pixels (HPs) is the first and key step for DS processing to overcome the low signal-to-noise ratio condition. Since multi-polarization data are good a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/21/5367 |
| _version_ | 1797466599731494912 |
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| author | Guanya Wang Kailiang Deng Qi Chen Zhiwei Li Han Gao Jun Hu Deliang Xiang |
| author_facet | Guanya Wang Kailiang Deng Qi Chen Zhiwei Li Han Gao Jun Hu Deliang Xiang |
| author_sort | Guanya Wang |
| collection | DOAJ |
| description | Distributed scatterers (DSs) are necessary to increase point density in multi-temporal InSAR (MT-InSAR) monitoring. The identification of homogeneous pixels (HPs) is the first and key step for DS processing to overcome the low signal-to-noise ratio condition. Since multi-polarization data are good at describing geometrical structures and dielectric properties of ground objects, they have been applied for HP identification. However, polarimetric information is not enough for identifying areas with similar ground objects but different deformation. We propose a novel DS preprocessing algorithm based on polarimetric interferometric homogeneous pixel (PIHP) identification. Firstly, a novel Polarimetric InSAR (PolInSAR) similarity that combines polarimetric intensity, interferometric coherence, and phase is proposed, which is readily available in multi-baseline and multi-polarization data and flexible by controlling weighting factors. Secondly, based on the binary partition tree (BPT) framework, object-orientated multi-scale PIHP identification is achieved, which is suitable for complex deformation scenes. Tested with simulated quad-polarization data, our method shows improvement in phase quality and point density, especially in the deformed areas, compared with the traditional HP identification method based on the polarimetric homogeneity (PolHom) test and the method with ground object type map. Tested with 30 quad-polarization Radarsat-2 images over Kilauea Volcano, the point density of our method is three times higher than that of the PolHom test in vegetation areas. Our method is proven to be more sensitive and mechanically more advanced to homogeneous pixels identification than the traditional ones, which is helpful for phase optimization, spatial enlargement of monitoring points, and stability of the MT-InSAR algorithm. |
| first_indexed | 2024-03-09T18:42:03Z |
| format | Article |
| id | doaj.art-6b9c6d7e5b1444a1848a25c050db9154 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:42:03Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6b9c6d7e5b1444a1848a25c050db91542023-11-24T06:37:51ZengMDPI AGRemote Sensing2072-42922022-10-011421536710.3390/rs14215367Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR DataGuanya Wang0Kailiang Deng1Qi Chen2Zhiwei Li3Han Gao4Jun Hu5Deliang Xiang6School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaNaval Research Institute, Tianjin 300061, ChinaChina Center for Resources Satellite Data and Application, Beijing 100094, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, ChinaSchool of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaBeijing Advanced Innovation Center for Soft Matter Science and Engineering, The Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing 100029, ChinaDistributed scatterers (DSs) are necessary to increase point density in multi-temporal InSAR (MT-InSAR) monitoring. The identification of homogeneous pixels (HPs) is the first and key step for DS processing to overcome the low signal-to-noise ratio condition. Since multi-polarization data are good at describing geometrical structures and dielectric properties of ground objects, they have been applied for HP identification. However, polarimetric information is not enough for identifying areas with similar ground objects but different deformation. We propose a novel DS preprocessing algorithm based on polarimetric interferometric homogeneous pixel (PIHP) identification. Firstly, a novel Polarimetric InSAR (PolInSAR) similarity that combines polarimetric intensity, interferometric coherence, and phase is proposed, which is readily available in multi-baseline and multi-polarization data and flexible by controlling weighting factors. Secondly, based on the binary partition tree (BPT) framework, object-orientated multi-scale PIHP identification is achieved, which is suitable for complex deformation scenes. Tested with simulated quad-polarization data, our method shows improvement in phase quality and point density, especially in the deformed areas, compared with the traditional HP identification method based on the polarimetric homogeneity (PolHom) test and the method with ground object type map. Tested with 30 quad-polarization Radarsat-2 images over Kilauea Volcano, the point density of our method is three times higher than that of the PolHom test in vegetation areas. Our method is proven to be more sensitive and mechanically more advanced to homogeneous pixels identification than the traditional ones, which is helpful for phase optimization, spatial enlargement of monitoring points, and stability of the MT-InSAR algorithm.https://www.mdpi.com/2072-4292/14/21/5367distributed scattererMT-InSARPIHPbinary partition treemulti-baseline PolInSAR |
| spellingShingle | Guanya Wang Kailiang Deng Qi Chen Zhiwei Li Han Gao Jun Hu Deliang Xiang Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data Remote Sensing distributed scatterer MT-InSAR PIHP binary partition tree multi-baseline PolInSAR |
| title | Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data |
| title_full | Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data |
| title_fullStr | Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data |
| title_full_unstemmed | Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data |
| title_short | Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data |
| title_sort | distributed scatterer processing based on binary partition trees with multi baseline polinsar data |
| topic | distributed scatterer MT-InSAR PIHP binary partition tree multi-baseline PolInSAR |
| url | https://www.mdpi.com/2072-4292/14/21/5367 |
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