New algorithms for shoreline monitoring from coastal video systems

Valentini, Nico; Saponieri, Alessandra; Molfetta, Matteo Gianluca; Damiani, Leonardo

doi:10.1007/s12145-017-0302-x

Cited by 44 publications

(39 citation statements)

References 44 publications

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“…Many authors highlighted the existence of different methodologies for coastal monitoring [6][7][8][9], not only limited to shoreline detection, based on direct and remote acquisition systems.…”

Section: Introductionmentioning

confidence: 99%

Statistical Deviations in Shoreline Detection Obtained with Direct and Remote Observations

Pugliano

Robustelli

Luccio

et al. 2019

JMSE

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Remote video imagery is widely used for shoreline detection, which plays a fundamental role in geomorphological studies and in risk assessment, but, up to now, few measurements of accuracy have been undertaken. In this paper, the comparison of video-based and GPS-derived shoreline measurements was performed on a sandy micro-tidal beach located in Italy (central Tyrrhenian Sea). The GPS survey was performed using a single frequency, code, and carrier phase receiver as a rover. Raw measurements have been post-processed by using a carrier-based positioning algorithm. The comparison between video camera and DGPS coastline has been carried out on the whole beach, measuring the error as the deviation from the DGPS line computed along the normal to the DGPS itself. The deviations between the two dataset were examined in order to establish possible spatial dependence on video camera point of view and on beach slope in the intertidal zone. The results revealed that, generally, the error increased with the distance from the acquisition system and with the wash up length (inversely proportional to the beach slope).

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Section: Introductionmentioning

confidence: 99%

Statistical Deviations in Shoreline Detection Obtained with Direct and Remote Observations

Pugliano

Robustelli

Luccio

et al. 2019

JMSE

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“…In the literature, the algorithms employed for coastal image classification need to distinguish between classes of pixels based on a limited number of intrinsic pixel features, normally the color channels RGB/HSV/Lab, and especially focused on seeds or regions of interest [13][14][15], or by means of a classifier which use features (e.g., geometrical, textural) that are typically discernible with the human eye (e.g., support vector machine, SVM [16]). In contrast to these shallow features, deep features which cannot be effectively extracted by traditional methodology have not yet played a role in coastal image classification.…”

Section: Image Classification-coastal Areamentioning

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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“…Remote sensing technology is an efficient and convenient mean for mapping global surface water at a large scale [11]. Different types of remote-sensing data (optical and radar) for surface water mapping have been widely documented [12,13]. In recent years, many scholars have conducted meaningful work on surface water mapping at a global scale, including the Global 3 arc-second Water Body Map (G3WBM) [9], the Global Land Cover Facility inland surface water map (GLCF-GIW) [3], the finer resolution observation and monitoring of the global land cover (FROM-GLC) water mask [14], the Global Land 30-water map [15] and high-resolution mapping of global surface water (HMGSW) [16].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

Jian

Long

et al. 2018

Remote Sensing

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Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25-100%, and the kappa coefficients of the MLP range from 0.965-1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1-7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accuracy of the classification. In future studies, the automation and universality of the proposed algorithm can be further enhanced with the generation of training samples based on newly-released global surface water products. Therefore, this method has the potential to map surface water based on Landsat series images or other high-resolution images and can be implemented for global surface water mapping, which will help us better understand our changing planet.

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New algorithms for shoreline monitoring from coastal video systems

Cited by 44 publications

References 44 publications

Statistical Deviations in Shoreline Detection Obtained with Direct and Remote Observations

Statistical Deviations in Shoreline Detection Obtained with Direct and Remote Observations

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

Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

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