RESUMEN
In this article, we propose an end-to-end deep network for the classification of multi-spectral time series and apply them to crop type mapping. Long short-term memory networks (LSTMs) are well established in this regard, thanks to their capacity to capture both long and short term temporal dependencies. Nevertheless, dealing with high intra-class variance and inter-class similarity still remain significant challenges. To address these issues, we propose a straightforward approach where LSTMs are combined with metric learning. The proposed architecture accommodates three distinct branches with shared weights, each containing a LSTM module, that are merged through a triplet loss. It thus not only minimizes classification error, but enforces the sub-networks to produce more discriminative deep features. It is validated via Breizhcrops, a very recently introduced and challenging time series dataset for crop type mapping.
RESUMEN
Placed within the context of content-based image retrieval, we study in this paper the potential of morphological operators as far as color description is concerned, a booming field to which the morphological framework, however, has only recently started to be applied. More precisely, we present three morphology-based approaches, one making use of granulometries independently computed for each subquantized color and two employing the principle of multiresolution histograms for describing color, using respectively morphological levelings and watersheds. These new morphological color descriptors are subsequently compared against known alternatives in a series of experiments, the results of which assert the practical interest of the proposed methods.