Automatic deforestation detectors based on frequentist statistics and their extensions for other spatial objects
This work addresses deforestation monitoring for environmental and remote sensing applications, but it appears incremental as it builds on existing statistical frameworks without major breakthroughs.
The paper tackles the problem of detecting forest and non-forest areas in Earth images by proposing two statistical methods based on frequentist statistics, including a novel parametric approach for broader applications like natural object and anomaly detection, and compares them with standard machine learning algorithms on satellite data, though no concrete performance numbers are provided.
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 practical aspects of their implementation. We also compare our algorithms with those from standard machine learning using satellite data.