Green Coverage Detection on Sub-orbital Plantation Images Using Anomaly Detection

The green coverage region is a relevant information to be extracted from remote sensing agriculture images. Automatic methods based on threshold and vegetation indices are often applied to address this task. However, sub-orbital remote sensing images have elements that can hinder the automatic analysis. Also, supervised methods can suffer from imbalance since there is often many more green coverage samples available than regions of gaps, weed and degraded areas. We propose an anomaly detection approach to deal with these challenges. Parametric anomaly detection methods using the normal distribution were used and compared with vegetation indices, unsupervised and supervised learning methods. The results showed that anomaly detection algorithms can handle better the green coverage detection. The proposed methods showed similar or better accuracy when compared with the competing methods. It deals well with different images and with the imbalance problem, confirming the practical application of the approach.