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A wide variety of applications of fundamental importance for security,environmental protection and urban development need access to accurate landcover monitoring, for which the analysis of optical remote sensing imagery iskey. Classification of time-series images, particularly with recursive methods,is of increasing interest in the current literature. Nevertheless, existingrecursive approaches typically require large amounts of training data. Thispaper introduces a recursive classification framework that improves thedecision-making process in multitemporal and multispectral land coverclassification algorithms while requiring low computational cost and minimalsupervision. The proposed approach allows the conversion of an instantaneousclassifier into a recursive Bayesian classifier by using a probabilisticframework that is robust to non-informative image variations. Three two-classexperiments are conducted using Sentinel-2 data. The first one consists in thewater mapping of an embankment dam in Oroville, California, USA; the second oneis a water mapping experiment of the Charles river basin area in Boston,Massachusetts, USA; and the last experiment addresses deforestation detectionin the Amazon rainforest. The spectral index classifier (SIC) is introduced asa method to convert broadband spectral indices such as the NDWI and the MNDWIinto probabilistic classification results. A classifier based on the Gaussianmixture model (GMM), a logistic regression (LR) classifier, and the SIC arecompared to their recursive counterparts. Results show that the use of Bayesianrecursion significantly increases the robustness of existing instantaneousclassifiers in multitemporal settings, including two state-of-the-art deeplearning-based models, without the need for additional training data.Furthermore, the proposed framework scales well for an increasing number ofclasses.

Recursive classification of satellite imaging time-series: An application to land cover mapping