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Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects
This article is devoted to the problem of detection of forest and nonforest areas on Earth images. We propose two statistical methods to tackle this problem: one based on multiple hypothesis testing with parametric distribution families, another one—on nonparametric tests. The parametric approach is novel in the literature and relevant to a larger class of problems—detection of natural objects, as well as anomaly detection. We develop mathematical background for each of the two methods, build self‐sufficient detection algorithms using them and discuss practical aspects of their implementation. We also compare our algorithms with each other and with those from standard machine learning using satellite data.
Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects
This article is devoted to the problem of detection of forest and nonforest areas on Earth images. We propose two statistical methods to tackle this problem: one based on multiple hypothesis testing with parametric distribution families, another one—on nonparametric tests. The parametric approach is novel in the literature and relevant to a larger class of problems—detection of natural objects, as well as anomaly detection. We develop mathematical background for each of the two methods, build self‐sufficient detection algorithms using them and discuss practical aspects of their implementation. We also compare our algorithms with each other and with those from standard machine learning using satellite data.
Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects
Muren, Jesper (author) / Niklasson, Vilhelm (author) / Otryakhin, Dmitry (author) / Romashin, Maxim (author)
Environmetrics ; 35
2024-08-01
20 pages
Article (Journal)
Electronic Resource
English
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