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- W4312363971 abstract "Deep learning techniques have been demonstrated with a pronounced performance in diverse object recognition and classification fields. An accurate distribution map of trees provides sufficient information on forest ecosystems and underpins the need for the sustainable management of forests. For classification with a smaller number of reference datasets, applications of the CNN technique with transfer learning and fine-tuning process were reported to be suitable in the literature. This study applied the Mask R-CNN technique to tree species mapping for a hemiboreal mixed forest in Hokkaido, Japan. An orthoimage with 25 cm resolution generated via airborne RGB image was used for this study. The Mask R-CNN model was derived from a 5-ha reference image and evaluated by a 1-ha test image. The experimental results show that a moderate accuracy (F1 score = 0.72) can be achieved for the forest with six dominant species in this study. The accuracy measure changed dramatically in the species, ranging from 0.20 to 0.94. This accuracy appeared to be a function of the sample size in the reference dataset for machine learning via high-resolution airborne RGB images. In this work, the species with sample images of more than 200 seem sufficient to achieve an F1 score greater than 0.90." @default.
- W4312363971 created "2023-01-04" @default.
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- W4312363971 date "2022-07-17" @default.
- W4312363971 modified "2023-10-03" @default.
- W4312363971 title "Tree Species Mapping of a Hemiboreal Mixed Forest Using Mask R-CNN" @default.
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- W4312363971 doi "https://doi.org/10.1109/igarss46834.2022.9884826" @default.
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