日本リモートセンシング学会誌
Online ISSN : 1883-1184
Print ISSN : 0289-7911
ISSN-L : 0289-7911
データ拡張による衛星ライダ波形の地盤高推定器の汎化性能獲得手法の提案
澤田 義人三橋 怜
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ジャーナル フリー 早期公開

論文ID: 2024.042

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Forests play an important role as carbon dioxide sinks and are said to contribute greatly to mitigating climate change. In recent years, with the development of Light Detection and Ranging (LiDAR) technology, it has become common to observe forest height and structure with high accuracy. Global Ecosystem Dynamics Investigation (GEDI), a space-based LiDAR system for vegetation observation, has been observing forest structure at the global level since 2019. Methods based on deep learning have been reported for the analysis of GEDI-received waveforms, in addition to conventional methods based on peak fitting. In tropical forests in particular, a major problem is the lack of detailed, wide-area, three-dimensional structural data that can be used to teach or validate GEDI waveform analysis. In deep learning image processing, data augmentation has been used when the amount of training data is small. In this study, we use waveform simulation results using relatively easy-to-obtain point cloud data from Japan as training data to build a deep learning machine with ground surface estimation performance for tropical evergreen forests, which are completely different forest types, using data augmentation. Assuming that no training data for tropical evergreen forests can be obtained, we created a data augmentation method for satellite LiDAR waveforms based on the concept of image data augmentation, and analyzed whether this method is effective even when the amount of training data is very small. Our method significantly improved both the average error and the root mean square error (RMSE) of ground surface estimation, even in tropical evergreen forests in the central Amazon. Furthermore, even when the amount of training data was reduced to 1/100, the present data augmentation method achieved the same estimation accuracy. Our method can greatly improve the accuracy of ground height estimation from satellite LiDAR waveforms even when detailed point cloud data that serve as training data are not available. Hence, it is highly useful for analyzing the three-dimensional structure of forests.

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