Machine learning and shoreline monitoring using optical satellite images: case study of the Mostaganem shoreline, Algeria

Bengoufa, Soumia; Niculescu, Simona; Mihoubi, Mustapha Kamel; Belkessa, Rabah; Rami, Ali; Rabehi, Walid; Abbad, Katia

doi:10.1117/1.jrs.15.026509

Cited by 23 publications

(10 citation statements)

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“…Masria et al (2015) utilized Landsat Satellite images to extract shoreline positions and estimate shoreline change rates of the Nile delta coast by using SVM with semantic segmentation [14]. Bengoufa et al (2021) utilized remotely sensed images to extract Mostaganem coastline (Algeria) by using SVM and random forest (RF) with semantic segmentation [15]. However, SVM is a linear classifier which may prone to constantly misclassify certain data.…”

Section: Literature Reviewmentioning

confidence: 99%

Shoreline Recognition Using Machine Learning Techniques

Zhong

El-Diraby

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Coastal areas have emerged to be the most significant and dynamic regions worldwide. Therefore, automating shoreline recognition will aid non-profit conservation authorities to reduce public budget expenditures, relieve erosion damage, and increase the climate resilience of the natural environment. In this paper, advanced ML boosting algorithms including XGBoost, and LGBM are firstly applied into shoreline recognition with aerial images (of Lake Ontario in this study). This paper first discussed the significance and a literature review of recent progress in shoreline detection. Then, this paper adopted semantic segmentation instead of detecting shoreline directly, which enables the (Machine Learning) ML model to achieve relatively high accuracy with a small amount of data. 5 high-resolution images are used for training the model in which shorelines are detected. The work was carried out in four steps: 1) labeling the contents of shoreline images as areas of water and banks; 2) training ML algorithms; 3) using the trained algorithms to classify the image content as either water or land objects; 4) post-processing by de-noising image pixels (applying a Fourier transform algorithm) to obtain a defined shoreline. The averaged training time per image for Random Forest, XGBoost, and LGBM algorithms are 195.2 sec, 71.0 sec, and 8.6 sec, respectively. The averaged accuracy is 95.6%, 96.0%, and 94.8%, respectively; the XGBoost algorithm has slightly higher accuracy, while LGBM has a significantly shorter runtime. Cross-validation of the LGBM algorithm reduced the training time by around 23% (7.0 sec) and increased the accuracy by only 1.1% (to 95.9%).

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Section: Literature Reviewmentioning

confidence: 99%

Shoreline Recognition Using Machine Learning Techniques

Zhong

El-Diraby

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…21); water indices, such as the normalized difference water index; 22 raster-based approaches, such as random forest; 23 and vector-based approaches, such as object-based image analysis. 24 These approaches usually produce a shoreline that follows the shape of the pixels, even though this pixel-bounded shoreline can be smoothed using vector generalizing algorithms. Shorelines extracted with pixel-based approaches could be refined to overcome their inherent pixelation, accomplished through a smoothing algorithm (e.g., the Snake energy minimization algorithm 25 ) or a simplification algorithm (e.g., the Douglas-Peucker reduction algorithm 26 ).…”

Section: Introductionmentioning

confidence: 99%

“…Pixel-based approaches can be divided into categories including band thresholding (e.g., Ref. 21); water indices, such as the normalized difference water index; 22 raster-based approaches, such as random forest; 23 and vector-based approaches, such as object-based image analysis 24 . These approaches usually produce a shoreline that follows the shape of the pixels, even though this pixel-bounded shoreline can be smoothed using vector generalizing algorithms.…”

Section: Introductionmentioning

confidence: 99%

On the shoreline positioning via remote sensing imagery: an isoradiometric approach

Maltese,

Caldareri,

Dardanelli

et al. 2024

J. Appl. Rem. Sens.

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Estimating the accurate position of the shoreline over time can be challenging. We propose a new approach to delimit the shoreline from remotely sensed images simply and accurately. This approach treats pixels as simultaneous radiometric measurements and, by examining the distance between iso-radiometry lines, assumes the shoreline in the position where the radiometric behavior changes more suddenly. The analyses conducted applying this coastline extraction approach underscore its significant flexibility. Specifically, the approach yields promising outcomes even if applied to a variety of different and dissimilar types of remotely sensed products. Indeed, the approach's intrinsic straightforwardness and low computational load qualify it as a promising tool for time-series production.

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“…The shoreline monitoring by traditional techniques is often a tedious and subjective task (Boak and Turner, 2005), indeed, automatic and reproducible techniques are needed (Bagli and Soille, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…In fact, an improved (semi)-automatic detection of the shoreline via remote sensed images processing could significantly optimize the time and costs, and reduce the subjective component of manual shoreline digitization. In this regard, many different research fields could benefit from this improved shoreline extraction, such as coastal vulnerability and erosion assessment, environmental risk analysis, land use planning, and coastal management and engineering (Bengoufa, et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Rocky Shoreline Extraction Using a Deep Learning Model and Object-Based Image Analysis

Bengoufa

Niculescu

Mihoubi

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the context of the increasing anthropogenic influence on the coastal areas that are subject to high climate variability, the main challenge is to understand its current dynamics and to predict its future evolution. Therefore, monitoring of the shoreline kinematics is a key factor for the coastal erosion assessment and an essential feature for the sustainable management of these naturally vulnerable areas.This work focuses on the detection and extraction of the shoreline, basing on a specific remote sensing methodology using Very High Resolution (VHR) optical images. Indeed, an integrated approach based on a Deep Learning model, which is the Convolutional Neural Network (CNN) and Object Based Image Analysis (OBIA) has been developed. This study aims to evaluate the methodological contribution of this integrated approach for the (semi)-automatic extraction of the rocky shoreline, for which the botanical indicator has been chosen. Therefore the upper limit of black marine lichen has been detected and extracted as the target shoreline. It is the first indication of a (semi)-automatic detection of such a complex type of shoreline.The classification results derived from the combined CNN model and OBIA methods had achieved a high overall accuracy of 0.94. The extracted shoreline have been compared to a shoreline of reference derived from a traditional method that is a manual digitizing. The distances between the two shorelines has been calculated in order to assess the accuracy of the extraction method. This comparison revealed that 76% of the extracted shoreline lies within 1 m, and 35% lies within 0.5 m of reference one. Therefore, the CNN model integrated to OBIA was successfully shown to be a good method for shoreline extraction and could offer an immediate insight regarding rocky shoreline position, providing an alternative to its monitoring.

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Machine learning and shoreline monitoring using optical satellite images: case study of the Mostaganem shoreline, Algeria

Cited by 23 publications

References 0 publications

Shoreline Recognition Using Machine Learning Techniques

Shoreline Recognition Using Machine Learning Techniques

On the shoreline positioning via remote sensing imagery: an isoradiometric approach

Rocky Shoreline Extraction Using a Deep Learning Model and Object-Based Image Analysis

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