Abstract
Coastal video monitoring has proven to be a valuable ground-based technique to investigate ocean processes. Presently, there is a growing need for automatic, technically efficient, and inexpensive solutions for image processing. Moreover, beach and coastal water quality problems are becoming significant and need attention. This study employs a methodological approach to exploit low-cost smartphone-based images for coastal image classification. The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring. A pixel-wise convolutional neural network (CNN) has demonstrated optimal performance in the classification of natural images by using abstracted deep features. Conventional CNNs demand a great deal of resources in terms of processing time and disk space. Therefore, CNN classification with superpixels has recently become a field of interest. In this work, a CNN-based deep learning framework is proposed that combines sticky-edge adhesive superpixels. The results indicate that a cheap camera-based video monitoring system is a suitable data source for coastal image classification, with optimal accuracy in the range between 75% and 96%. Furthermore, an application of the method for an ongoing case study related to Sargassum monitoring in the French Antilles proved to be very effective for developing a warning system, aiming at evaluating floating algae and algae that had washed ashore, supporting municipalities in beach management.
Highlights
Around 3000 superpixels for each image were used as processing units for convolutional neural network (CNN) training
The metric used in this work for analyzing the performance of the methodology presented is mainly based on a confusion matrix, a table with rows and columns reporting the number of false positives (FP), false negatives (FN), true positives (TP), and true negatives (TN), which allows for more detailed analysis than the mere proportion of correct classifications
The classification accuracy was evaluated with respect to the superpixel-based CNN classification, and by inspecting the pixel-scale results, following conditional random field (CRF) refinement
Summary
Among the coastal video monitoring techniques, ground-sensing approaches by means of camera systems are widely used, allowing the investigation of coastal processes with high temporal and spatial resolution, with optimal accuracy. When dealing with coastal ground-based camera systems, with respect to satellite observations, the availability of images with smaller but optimal field-of-view and proper spatial resolution increases the opportunity of removing the barriers related to, for example, cloud cover and temporal lags, facilitating the study of large time series. As a result, this requires focus be placed on two characteristics in particular: automation and pixel-based distinctiveness
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