Machine learning and shoreline monitoring using optical satellite images: case study of the Mostaganem shoreline, Algeria
| Autor: | Mustapha Kamel Mihoubi, Soumia Bengoufa, Rabah Belkessa, Ali Rami, Katia Abbad, Simona Niculescu, Walid Rabehi |
|---|---|
| Přispěvatelé: | Littoral, Environnement, Télédétection, Géomatique (LETG - Brest), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-Université de Nantes (UN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
0211 other engineering and technologies Image processing 02 engineering and technology Machine learning computer.software_genre 01 natural sciences 14. Life underwater ComputingMilieux_MISCELLANEOUS 021101 geological & geomatics engineering 0105 earth and related environmental sciences Shore geography geography.geographical_feature_category Contextual image classification Pixel business.industry Image segmentation [SHS.GEO]Humanities and Social Sciences/Geography Random forest Support vector machine Feature (computer vision) General Earth and Planetary Sciences Artificial intelligence business computer Geology |
| Zdroj: | Journal of applied remote sensing Journal of applied remote sensing, Bellingham, WA : SPIE, 2021, 15 (02), ⟨10.1117/1.JRS.15.026509⟩ |
| ISSN: | 1931-3195 |
| DOI: | 10.1117/1.JRS.15.026509⟩ |
| Popis: | Coastal monitoring is an essential feature for the sustainable management of naturally vulnerable areas; however, data acquisition is a tedious task. We aim to identify an efficient method of automatic shoreline monitoring based on high water level detection using very high-resolution Pleiades images and taking as the pilot zone the Mostaganem coastline (Algeria). Through a comparative study between classification methods based on pixel- and object-based image analyses (PBIA and OBIA, respectively), algorithmic development and optimizing was conducted on two machine learning (ML) classifiers: random forest (RF), and support vector machine (SVM), and two segmentation algorithms: multiresolution (MRS) and meanshift (MSS). These classification methods yielded six different shorelines that were validated using an in-situ GPS survey shoreline acquired on the same day as the Pleiades image. The results showed that the OBIA generated a shoreline with a 5% to 25% better accuracy than that of PBIA using the same ML algorithm. Within the OBIA approach, MRS generated a shoreline with 20% higher accuracy compared to MSS, suggesting the importance of segmentation possessing. The RF based on MRS was the method that produced the shoreline at the best accuracy, where 55.5% of the extracted shoreline was within 1 pixel of the in situ shoreline. This method was successfully shown to be a good alternative for shoreline monitoring of sandy microtidal coasts, offering to coastal managers a reliable tool to complete the data and efficiently manage the coastal erosion. |
| Databáze: | OpenAIRE |
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