Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features
Full-waveform spaceborne laser altimeter can provide more characteristic parameters of the laser footprint and rich vertical structure information on the target surface. This technology has the potential for land-cover classification, especially in hard-to-reach mountain areas. Classifying the land...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9713978/ |
| _version_ | 1819159785060171776 |
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| author | Xiangfeng Liu Xiaodan Liu Zhenhua Wang Genghua Huang Rong Shu |
| author_facet | Xiangfeng Liu Xiaodan Liu Zhenhua Wang Genghua Huang Rong Shu |
| author_sort | Xiangfeng Liu |
| collection | DOAJ |
| description | Full-waveform spaceborne laser altimeter can provide more characteristic parameters of the laser footprint and rich vertical structure information on the target surface. This technology has the potential for land-cover classification, especially in hard-to-reach mountain areas. Classifying the land types based on the returned waveform can provide a convenient way for the online classification needs and assess the quality of footprint used as the ground control point in photogrammetry. This article presents a random forest (RF) classification using geoscience laser altimeter system waveform, in the west-central Yunnan Province, China. First, an improved threshold wavelet is performed to denoise the waveform, and then Gaussian decomposition is used to extract the typical characteristic features of footprint. Second, an RF algorithm is implemented to clarify the footprints into five types: flat, building, terrace, forest, and mountain. Finally, quantitative analysis is conducted with producer's accuracy (PA), user's accuracy (UA), overall accuracy (OA), precision, recall rate, F1-score, and kappa coefficient to compare the performance of RF with other classifiers, including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), logistic regression (LR), K-nearest neighbor (KNN), and naive Bayes (NB). The results show that all the six methods can accurately classify the flat land with 100.00% PA and UA. The RF also has the best performances in other four land types, with PA and UA of 98.14% and 100.00%, 97.24% and 95.49%, 98.64% and 96.03%, and 94.64% and 100.00%, respectively. The OA, precision, recall, F1-score, and kappa coefficient for the RF are 97.95%, 97.73%, 98.30%, 97.99%, and 0.9737, respectively; while 83.45%, 82.55%, 82.98%, 81.16%, and 0.7765 for NB, which has the worst performance. LR performs better than RBF-SVM, linear-SVM, and KNN. It also observes worse classification accuracy for all methods when the waveforms are more complex. |
| first_indexed | 2024-12-22T16:46:04Z |
| format | Article |
| id | doaj.art-9a7e8766778f4a6f9bf24d5e5eca404c |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-22T16:46:04Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-9a7e8766778f4a6f9bf24d5e5eca404c2022-12-21T18:19:44ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01152284229710.1109/JSTARS.2022.31513329713978Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform FeaturesXiangfeng Liu0https://orcid.org/0000-0002-6821-449XXiaodan Liu1Zhenhua Wang2https://orcid.org/0000-0002-6603-2019Genghua Huang3Rong Shu4https://orcid.org/0000-0002-3620-4617Key Laboratory of Space Active Opto-Electronics Technology, Chinese Academy of Sciences (CAS), Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaKey Laboratory of Space Active Opto-Electronics Technology, Chinese Academy of Sciences (CAS), Shanghai, ChinaKey Laboratory of Space Active Opto-Electronics Technology, Chinese Academy of Sciences (CAS), Shanghai, ChinaFull-waveform spaceborne laser altimeter can provide more characteristic parameters of the laser footprint and rich vertical structure information on the target surface. This technology has the potential for land-cover classification, especially in hard-to-reach mountain areas. Classifying the land types based on the returned waveform can provide a convenient way for the online classification needs and assess the quality of footprint used as the ground control point in photogrammetry. This article presents a random forest (RF) classification using geoscience laser altimeter system waveform, in the west-central Yunnan Province, China. First, an improved threshold wavelet is performed to denoise the waveform, and then Gaussian decomposition is used to extract the typical characteristic features of footprint. Second, an RF algorithm is implemented to clarify the footprints into five types: flat, building, terrace, forest, and mountain. Finally, quantitative analysis is conducted with producer's accuracy (PA), user's accuracy (UA), overall accuracy (OA), precision, recall rate, F1-score, and kappa coefficient to compare the performance of RF with other classifiers, including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), logistic regression (LR), K-nearest neighbor (KNN), and naive Bayes (NB). The results show that all the six methods can accurately classify the flat land with 100.00% PA and UA. The RF also has the best performances in other four land types, with PA and UA of 98.14% and 100.00%, 97.24% and 95.49%, 98.64% and 96.03%, and 94.64% and 100.00%, respectively. The OA, precision, recall, F1-score, and kappa coefficient for the RF are 97.95%, 97.73%, 98.30%, 97.99%, and 0.9737, respectively; while 83.45%, 82.55%, 82.98%, 81.16%, and 0.7765 for NB, which has the worst performance. LR performs better than RBF-SVM, linear-SVM, and KNN. It also observes worse classification accuracy for all methods when the waveforms are more complex.https://ieeexplore.ieee.org/document/9713978/Classificationfull-waveformGaussian decompositionlaser footprintrandom forest (RF)spaceborne laser altimeter |
| spellingShingle | Xiangfeng Liu Xiaodan Liu Zhenhua Wang Genghua Huang Rong Shu Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification full-waveform Gaussian decomposition laser footprint random forest (RF) spaceborne laser altimeter |
| title | Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features |
| title_full | Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features |
| title_fullStr | Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features |
| title_full_unstemmed | Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features |
| title_short | Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features |
| title_sort | classification of laser footprint based on random forest in mountainous area using glas full waveform features |
| topic | Classification full-waveform Gaussian decomposition laser footprint random forest (RF) spaceborne laser altimeter |
| url | https://ieeexplore.ieee.org/document/9713978/ |
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