The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data
Laser return intensity (LRI) information obtained from airborne laser scanning (ALS) data has been used to classify land cover types and to reveal canopy physiological features. However, the sensor-related and environmental parameters may introduce noise. In this study, we developed a local median f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/10/1681 |
| _version_ | 1797567161216008192 |
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| author | Bingxiao Wu Guang Zheng Weimin Ju |
| author_facet | Bingxiao Wu Guang Zheng Weimin Ju |
| author_sort | Bingxiao Wu |
| collection | DOAJ |
| description | Laser return intensity (LRI) information obtained from airborne laser scanning (ALS) data has been used to classify land cover types and to reveal canopy physiological features. However, the sensor-related and environmental parameters may introduce noise. In this study, we developed a local median filtering (LMF) method to point-by-point correct the LRI information. For each point, we deduced the reference variation range for its LRI. Then, we replaced the outliers of LRI with their local median values. To evaluate the LMF method, we assessed the discrepancy of LRI information from the same and diverse land cover types. Moreover, we used the corrected LRI to distinguish points from grass, road, and bare land, which were classified as ground type in ALS data. The results show that using the LMF method could increase the similarity of pointwise LRI from the same land cover type and the discrepancy of those from different kinds of targets. Using the LMF-corrected LRI could improve the overall classification accuracy of three land cover types by about 3% (all over 81%, <i>κ</i> ≥ 0.73, <i>p</i> < 0.05), compared to those using the original and range-normalized LRI. The sensor-related metrics brought more noise to the original LRI information than the environmental factors. Using the LMF method could effectively correct LRI information from historical ALS datasets. |
| first_indexed | 2024-03-10T19:37:44Z |
| format | Article |
| id | doaj.art-6e330bcdc65a4169b04f1554ba51763a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:37:44Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6e330bcdc65a4169b04f1554ba51763a2023-11-20T01:35:10ZengMDPI AGRemote Sensing2072-42922020-05-011210168110.3390/rs12101681The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning DataBingxiao Wu0Guang Zheng1Weimin Ju2International Institute for Earth System Science, Nanjing University, Nanjing 210023, ChinaInternational Institute for Earth System Science, Nanjing University, Nanjing 210023, ChinaInternational Institute for Earth System Science, Nanjing University, Nanjing 210023, ChinaLaser return intensity (LRI) information obtained from airborne laser scanning (ALS) data has been used to classify land cover types and to reveal canopy physiological features. However, the sensor-related and environmental parameters may introduce noise. In this study, we developed a local median filtering (LMF) method to point-by-point correct the LRI information. For each point, we deduced the reference variation range for its LRI. Then, we replaced the outliers of LRI with their local median values. To evaluate the LMF method, we assessed the discrepancy of LRI information from the same and diverse land cover types. Moreover, we used the corrected LRI to distinguish points from grass, road, and bare land, which were classified as ground type in ALS data. The results show that using the LMF method could increase the similarity of pointwise LRI from the same land cover type and the discrepancy of those from different kinds of targets. Using the LMF-corrected LRI could improve the overall classification accuracy of three land cover types by about 3% (all over 81%, <i>κ</i> ≥ 0.73, <i>p</i> < 0.05), compared to those using the original and range-normalized LRI. The sensor-related metrics brought more noise to the original LRI information than the environmental factors. Using the LMF method could effectively correct LRI information from historical ALS datasets.https://www.mdpi.com/2072-4292/12/10/1681intensity correctionlaser radiation effectairborne laser scanningradar equation |
| spellingShingle | Bingxiao Wu Guang Zheng Weimin Ju The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data Remote Sensing intensity correction laser radiation effect airborne laser scanning radar equation |
| title | The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data |
| title_full | The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data |
| title_fullStr | The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data |
| title_full_unstemmed | The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data |
| title_short | The Local Median Filtering Method for Correcting the Laser Return Intensity Information from Discrete Airborne Laser Scanning Data |
| title_sort | local median filtering method for correcting the laser return intensity information from discrete airborne laser scanning data |
| topic | intensity correction laser radiation effect airborne laser scanning radar equation |
| url | https://www.mdpi.com/2072-4292/12/10/1681 |
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