  A wide variety of applications of fundamental importance for security,
environmental protection and urban development need access to accurate land
cover monitoring, for which the analysis of optical remote sensing imagery is
key. Classification of time-series images, particularly with recursive methods,
is of increasing interest in the current literature. Nevertheless, existing
recursive approaches typically require large amounts of training data. This
paper introduces a recursive classification framework that improves the
decision-making process in multitemporal and multispectral land cover
classification algorithms while requiring low computational cost and minimal
supervision. The proposed approach allows the conversion of an instantaneous
classifier into a recursive Bayesian classifier by using a probabilistic
framework that is robust to non-informative image variations. Three two-class
experiments are conducted using Sentinel-2 data. The first one consists in the
water mapping of an embankment dam in Oroville, California, USA; the second one
is a water mapping experiment of the Charles river basin area in Boston,
Massachusetts, USA; and the last experiment addresses deforestation detection
in the Amazon rainforest. The spectral index classifier (SIC) is introduced as
a method to convert broadband spectral indices such as the NDWI and the MNDWI
into probabilistic classification results. A classifier based on the Gaussian
mixture model (GMM), a logistic regression (LR) classifier, and the SIC are
compared to their recursive counterparts. Results show that the use of Bayesian
recursion significantly increases the robustness of existing instantaneous
classifiers in multitemporal settings, including two state-of-the-art deep
learning-based models, without the need for additional training data.
Furthermore, the proposed framework scales well for an increasing number of
classes.
