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A data-noise tolerant method for multi-temporal hyperspectral images classification

Remote sensing data is not perfect and can be affected by some corruptions that may alter the data consistency, models created from the data and decisions made based on these models. Estimation error can diminish system efficiency in terms of classification accuracy. Accordingly, some learning approaches have adopted different ways to enhance their learning abilities from erroneous environments. Nevertheless, the existence of interpolation error can still introduce critical conflicting decisions. A more reasonable solution is introduced in this paper by incorporating this error into learning process. Instead of taking any unified theory of noise to evaluate the noise impacts separately, we try and to estimate this error and to incorporate it, cooperatively, into learning stage in order to enhance classification accuracy. Concretely, we present a new way to deal with reconstruction error when classifying 3D spectral signatures.