Anomaly Detection in Post Fire Assessment

Over the last few years, natural disasters elevated dangerously in terms of immensity and prevalence over areas covered by forest and urban woodlands. Fast-spreading nature of the wildfires determine quick uncontrollable situations' causing significant effects in short periods. Despite increased difficulty in image processing approaches due to temporal resolution, complexity of spectral bands and illumination conditions, imagery data streams available from sun-synchronous satellites provide geospatial intelligence in monitoring and preventing fire threats. In this paper, we proposed a local scale burned area estimation framework that employs multispectral images in a deep learning architecture for detecting burned surfaces at patch level. This goal is accomplished by using an autoencoder (AE) network in which the latent feature layer learns normal background distribution, beneficial to background reconstruction. Furthermore, an outlier detection method (OCSVM) is used with aggregated features, latent and covariance components, in order to estimate burned coverage. Our method operates on data retrieved from Sentinel-2 (S2) constellation streaming source, which mainly contain normal scenes and limited fire affected spots