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Fast automatic deforestation detectors and their extensions for other spatial objects

2021-12-02 09:08:38
Jesper Muren, Vilhelm Niklasson, Dmitry Otryakhin, Maxim Romashin
arXiv_CV
       
Abstract

This paper is devoted to the problem of detection of forest and non-forest 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 non-parametric 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 numerical aspects of their implementation. We also compare our algorithms with those from standard machine learning using satellite data.

Abstract (translated)

URL

https://arxiv.org/abs/2112.01063

PDF

https://arxiv.org/pdf/2112.01063.pdf

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