LAUSR

In the past decade, anomaly detection has experienced an expanding attraction in satellite data analysis. Monitoring wildfire dynamics plays a substantial part in global land management, i.e., to detect and determine the expansion of such areas, estimate the deterioration of forest regions, and assist the intervention plan. In this article, we proposed an approach for Sentinel-2 scenes that detects anomalies in burned area contexts using a rank-ordered method on a single post-event image. We adopted a self-supervised paradigm in learning image representations by training a deep convolutional model to differentiate in a series of geometric transformations. Dirichlet distributions are selected as priors to characterize the variability of the random multinomial distribution in multispectral data. Dirichlet precision parameters are computed from observed data and are used to construct a ranking function that quantifies the degree of anomaly in data based on the softmax responses given by the trained classifier. We evaluated the performance of the proposed method through a cumulative effort of two remote sensing tasks, namely, open-set detection (i.e., test datasets contain classes unseen at the training time) and location separation (i.e., test datasets include images from distinct spatial location than the training images). The experiments were performed on three different datasets, BigEarthNet, and two actual burned area Sentinel-2 datasets from predisposed zones to fire events, Australia and Bolivia. We achieved in all test datasets state-of-the-art performance, considering the substantial and diversified types of natural anomalies in multispectral data.