Đã lưu trong:
| Những tác giả chính: | , |
|---|---|
| Định dạng: | Preprint |
| Được phát hành: |
2020
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| Những chủ đề: | |
| Truy cập trực tuyến: | https://arxiv.org/abs/2012.00180 |
| Các nhãn: |
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Mục lục:
- Understanding forest fire spread in any region of Canada is critical to promoting forest health, and protecting human life and infrastructure. Quantifying fire spread from noisy images, where regions of a fire are separated by change-point boundaries, is critical to faithfully estimating fire spread rates. In this research, we develop a statistically consistent smooth estimator that allows us to denoise fire spread imagery from micro-fire experiments. We develop an anisotropic smoothing method for change-point data that uses estimates of the underlying data generating process to inform smoothing. We show that the anisotropic local constant regression estimator is consistent with convergence rate $O\left(n^{-1/{(q+2)}}\right)$. We demonstrate its effectiveness on simulated one- and two-dimensional change-point data and fire spread imagery from micro-fire experiments.