Investigation on the Capabilities of Low-Cost and Smartphone-Based Coastal Imagery for Deriving Coastal State Video Indicators: Applications on the Upper Mediterranean

Valentini, Nico; Balouin, Yann; Laigre, Thibault; Bouvier, Clément; Belon, Rémi; Saponieri, Alessandra

doi:10.1142/9789811204487_0226

Cited by 1 publication

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“…Because only a few images are required for training since the superpixel partitioning creates a broad and adequate database for CNN transfer learning (i.e., the number of training superpixels in Table 1), easy prototyping of models can be realized. Applications related to shoreline detections and/or nearshore bars evolution, beach widths, dune growth, or Posidonia banquettes dynamics [53] could result in effortless automation. With respect to coastal classification algorithms (e.g., based on SVM [16]), the transfer learning method described here allowed us to employ 10× less ground-truth data while still achieving around the same classification accuracy [16].…”

Section: Discussionmentioning

confidence: 99%

Assessment of a Smartphone-Based Camera System for Coastal Image Segmentation and Sargassum monitoring

Valentini

Balouin

2020

JMSE

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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.

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Section: Discussionmentioning

confidence: 99%