The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data
The radiometric terrain correction (RTC) is an essential processing step for supervised classification applications of polarimetric synthetic aperture radar (PolSAR) over mountainous areas. However, the current angular variation effect (AVE) correction methods of three-step RTC processing are diffic...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/3/595 |
| _version_ | 1827659037508894720 |
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| author | Lei Zhao Erxue Chen Zengyuan Li Yaxiong Fan Kunpeng Xu |
| author_facet | Lei Zhao Erxue Chen Zengyuan Li Yaxiong Fan Kunpeng Xu |
| author_sort | Lei Zhao |
| collection | DOAJ |
| description | The radiometric terrain correction (RTC) is an essential processing step for supervised classification applications of polarimetric synthetic aperture radar (PolSAR) over mountainous areas. However, the current angular variation effect (AVE) correction methods of three-step RTC processing are difficult to apply to PolSAR supervised classification because of the problem of interdependence between AVE correction and classification. To address this issue, based on the three-step semi-empirical RTC approach, we propose an improved AVE correction method suitable for the supervised classification of PolSAR. We make full use of the prior knowledge required for supervised classification and RTC processing, that is, samples and elevation data, to calculate the parameters of AVE correction by constructing a weight coefficient matrix. GaoFen-3 QPSI (C-band, quad-polarization) data were used to verify the proposed method. Experimental results showed that the proposed method is available and effective for PolSAR supervised classification. The new method can effectively remove the AVE effect in the PolSAR image, and the overall accuracy of PolSAR supervised classification can be improved about 9% compared to that without AVE correction. For the fine classification of forest types, the AVE correction can improve the classification accuracy by about 20%. |
| first_indexed | 2024-03-09T23:13:56Z |
| format | Article |
| id | doaj.art-b4ddf9a3a66f40cd97fda8be8362dc20 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:13:56Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b4ddf9a3a66f40cd97fda8be8362dc202023-11-23T17:40:19ZengMDPI AGRemote Sensing2072-42922022-01-0114359510.3390/rs14030595The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR DataLei Zhao0Erxue Chen1Zengyuan Li2Yaxiong Fan3Kunpeng Xu4Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaThe radiometric terrain correction (RTC) is an essential processing step for supervised classification applications of polarimetric synthetic aperture radar (PolSAR) over mountainous areas. However, the current angular variation effect (AVE) correction methods of three-step RTC processing are difficult to apply to PolSAR supervised classification because of the problem of interdependence between AVE correction and classification. To address this issue, based on the three-step semi-empirical RTC approach, we propose an improved AVE correction method suitable for the supervised classification of PolSAR. We make full use of the prior knowledge required for supervised classification and RTC processing, that is, samples and elevation data, to calculate the parameters of AVE correction by constructing a weight coefficient matrix. GaoFen-3 QPSI (C-band, quad-polarization) data were used to verify the proposed method. Experimental results showed that the proposed method is available and effective for PolSAR supervised classification. The new method can effectively remove the AVE effect in the PolSAR image, and the overall accuracy of PolSAR supervised classification can be improved about 9% compared to that without AVE correction. For the fine classification of forest types, the AVE correction can improve the classification accuracy by about 20%.https://www.mdpi.com/2072-4292/14/3/595polarimetric SARradiometric terrain correctionsupervised classificationangular variation effect |
| spellingShingle | Lei Zhao Erxue Chen Zengyuan Li Yaxiong Fan Kunpeng Xu The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data Remote Sensing polarimetric SAR radiometric terrain correction supervised classification angular variation effect |
| title | The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data |
| title_full | The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data |
| title_fullStr | The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data |
| title_full_unstemmed | The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data |
| title_short | The Improved Three-Step Semi-Empirical Radiometric Terrain Correction Approach for Supervised Classification of PolSAR Data |
| title_sort | improved three step semi empirical radiometric terrain correction approach for supervised classification of polsar data |
| topic | polarimetric SAR radiometric terrain correction supervised classification angular variation effect |
| url | https://www.mdpi.com/2072-4292/14/3/595 |
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