# MEC: A Mesoscale Events Classifier for Oceanographic Imagery
### State of the Art  
The automatic identification and segmentation of ocean upwelling regions in EBUEs is a topic extensively tackled in previous literature. Traditionally, experts have identified and catalogued upwelling features by subjectively analysing sea-surface temperature (SST) maps of the area of interest. Upwelling indices have been exploited to provide a first guess directing the experts towards the dates and events to be analysed [19], but a visual inspection has always been needed to ascertain the presence of upwelling and to describe it. This procedure is manageable if tens or even hundreds of images are used, but it turns into an unfeasible task as the number of scenes approaches the thousands of images, a typical order of magnitude when the purpose is to investigate climate-related changes. Nowadays, with the growing amount of remote sensing observations, automated techniques have been gaining momentum. A common issue within this context is the accurate detection of the oceanic front, i.e., the narrow boundary separating the offshore waters from the upwelling area, located close to the shoreline. Among the considered approaches two primary schools of thought emerge: a classical one, exploiting standard computer vision tools, and a more contemporary one, based on machine learning tools, such as convolutional neural networks. Among the techniques belonging to the first group it is worth mentioning those devoted to the clustering of texture-related features, such as those based on the two-dimensional wavelet transform [20; 21] or gray-level co-occurrence matrices and derived texture measurements [22; 23]. Along these lines, in [24], the authors present an algorithm to group SST pixels based on a seed region growing approach. The algorithm performs the grouping of pixels in clusters, with the number of clusters not fixed a priori. Following an iterative procedure, each cluster is enlarged by including novel candidates that fulfil a similarity constraint. The process stops as soon as the similarity condition no longer holds. In [25], the authors propose a segmentation method based on a fuzzy-C-means clustering approach, that enables to identify the upwelling area in front of the northwestern African coast, through the analysis of the related SST imagery. In this case, the proposed novelty consists in the introduction of a pre-processing stage where the signal is previously normalized in order to reduce the effect of a non-negligible temperature offset, which varies with the latitude. The cancellation of this spurious trend allows to obtain an accurate separation between offshore and upwelling areas. Alternative approaches address mathematical morphology operators and mobile windows [26; 27]. Different approaches take advantage of the amplitude of the SST gradient across the upwelling boundaries, as in [28; 29; 30]. In [31] the authors describe a modified version of the Canny edge detector [32], implemented to detect the oceanic front boundary. The proposed method has been tested on input imagery that is not provided by real remote sensing campaigns but is actually returned as the output of forecasting models [33; 34], in the specific case study related to the South China Sea area. The recent progresses in the implementation of neural networks architectures encouraged the proposal of novel solutions to the upwelling identification problem, based on a machine learning approach. In [35], the authors exploit neural networks-based algorithms to identify the oceanic boundary and segment the upwelling image subset, while in [36] the authors exploit the currently trending Deep Neural Network (DNN) approach using an adapted version of the GoogleNet network [37]. In the context of remote sensed data, DNNs proved to be useful also to identify other mesoscale oceanic phenomena, such as eddies. For this purpose, the classification has been performed by adapting image processing methods to satellite altimetry data [38].