Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach
<p>Geolocalization of the study areas: the rectangles delimit the three study areas located respectively in Campania (red), Sardinia (yellow) and Sicily (green); the map is in equirectangular projection and WGS 84 ellipsoidal coordinates (EPSG: 4326).</p> "> Figure 2
<p>The 3 study areas in false color compositions of the Landsat 9 OLI images in UTM/WGS 84 plane coordinates (EPSG: 32633): Campania on the <b>left</b>, Sardinia on the <b>right</b> and Sicily on the <b>bottom</b>.</p> "> Figure 3
<p>The 3 study areas, in false color compositions of the Sentinel-2A images in UTM/WGS 84 plane coordinates (EPSG: 32633): Campania on the <b>left</b>, Sardinia on the <b>right</b> and Sicily on the <b>bottom</b>.</p> "> Figure 4
<p>Workflow of the adopted approach.</p> "> Figure 5
<p>Landsat 9 OLI synthetic bands for Campania study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 6
<p>Landsat 9 OLI synthetic bands for Sardinia study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 7
<p>Landsat 9 OLI synthetic bands for Sicily study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 8
<p>Sentinel-2 (20 m) synthetic bands for Campania study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 9
<p>Sentinel-2 (20 m) synthetic bands for Sardinia study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 10
<p>Sentinel-2 (20 m) synthetic bands for Sicily study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 11
<p>Sentinel-2 (10 m) synthetic bands for Campania study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 12
<p>Sentinel-2 (10 m) synthetic bands for Sardinia study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 13
<p>Sentinel-2 (10 m) synthetic bands, Sicily study area: PCA-1 on the <b>left</b>, NDWI on the <b>right</b> and MNDWI on the <b>bottom</b>.</p> "> Figure 14
<p>Geolocation of the selected zones to show details of K-means results: Port of Naples (Zone A) on the <b>left</b> and coastal area of San Teodoro (Zone B) on the <b>right</b>.</p> "> Figure 15
<p>K-means clustering in Zone A (Port of Naples) for synthetic images derived by L9: results for PCA-1 image on the <b>left</b> and for NDWI image on the <b>right</b>.</p> "> Figure 16
<p>K-means clustering in Zone B (coastal area of San Teodoro) for synthetic images derived by –m: results for PCA-1 image (on the <b>left</b>) and for NDWI image (on the <b>right</b>).</p> "> Figure 17
<p>Geolocation of the selected zones to show details of automatically vectorized coastlines obtained through PCA and through NDWI: Torre del Greco (Zone C) on the <b>left</b> and San Giovanni (Zone D) on the <b>right</b>.</p> "> Figure 18
<p>Details of automatically vectorized coastlines extracted from Landsat 9 OLI images in the area of Torre del Greco (Zone C): on the <b>left</b> the coastline from PCA (in green), on the <b>right</b> the coastline from NDWI (in blue); in both cases the reference coastline resulting from RGB visual interpretation and manual vectorization (in red) is reported for comparison.</p> "> Figure 19
<p>Details of automatically vectorized coastlines extracted from S2–20 m in the area of San Giovanni (Zone D): on the <b>left</b> the coastline from PCA (in green), on the <b>right</b> the coastline from NDWI (in blue); in both cases the reference coastline resulting from RGB visual interpretation and manual vectorization (in red) is reported for comparison.</p> "> Figure 20
<p>Details of automatically vectorized coastlines extracted from –m in the area of San Giovanni (Zone D): on the <b>left</b> the coastline from PCA (in green), on the <b>right</b> the coastline from NDWI (in blue); in both cases the reference coastline resulting from RGB visual interpretation and manual vectorization (in red) is reported for comparison.</p> ">
Abstract
:1. Introduction
2. Datasets
2.1. Landsat 9
2.2. Sentinel-2
3. Methods
3.1. Principal Component Analysis
3.2. K-Means Clustering
3.3. Coastline Extraction
- the purpose of our article is only to provide a method for extracting the instantaneous coastline from satellite images;
- the satellite images used (Landsat 9 OLI and Sentinel-2) have such a geometric resolution that the tidal variation for the areas examined is not appreciable.
3.4. Accuracy Assessment
3.5. Result Comparison
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Eugenio, F.; Martin, J.; Marcello, J.; Fraile-Nuez, E. Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery. Int. J. Appl. Earth Obs. Geoinf. 2014, 29, 53–66. [Google Scholar] [CrossRef]
- John, E.; Bunting, P.; Hardy, A.; Silayo, D.S.; Masunga, E. A Forest Monitoring System for Tanzania. Remote Sens. 2021, 13, 3081. [Google Scholar] [CrossRef]
- Aguilar, M.A.; Jiménez-Lao, R.; Ladisa, C.; Aguilar, F.J.; Tarantino, E. Comparison of spectral indices extracted from Sentinel-2 images to map plastic covered greenhouses through an object-based approach. GISci. Remote Sens. 2022, 59, 822–842. [Google Scholar] [CrossRef]
- Mohanty, B.P.; Cosh, M.H.; Lakshmi, V.; Montzka, C. Soil moisture remote sensing: State-of-the-science. Vadose Zone J. 2017, 16, 1–9. [Google Scholar] [CrossRef]
- Paul, F.; Winsvold, S.H.; Kääb, A.; Nagler, T.; Schwaizer, G. Glacier remote sensing using Sentinel-2. Part II: Mapping glacier extents and surface facies, and comparison to Landsat 8. Remote Sens. 2016, 8, 575. [Google Scholar] [CrossRef]
- Gennaro, A.; Candiano, A.; Fargione, G.; Mangiameli, M.; Mussumeci, G. Multispectral remote sensing for post-dictive analysis of archaeological remains. A case study from Bronte (Sicily). Archaeol. Prospect. 2019, 26, 299–311. [Google Scholar] [CrossRef]
- Alicandro, M.; Candigliota, E.; Dominici, D.; Immordino, F.; Masin, F.; Pascucci, N.; Quaresima, R.; Zollini, S. Hyperspectral PRISMA and Sentinel-2 Preliminary Assessment Comparison in Alba Fucens and Sinuessa Archaeological Sites (Italy). Land 2022, 11, 2070. [Google Scholar] [CrossRef]
- Solari, L.; Del Soldato, M.; Raspini, F.; Barra, A.; Bianchini, S.; Confuorto, P.; Casagli, N.; Crosetto, M. Review of satellite interferometry for landslide detection in Italy. Remote Sens. 2020, 12, 1351. [Google Scholar] [CrossRef]
- Fabris, M. Monitoring the coastal changes of the Po River delta (Northern Italy) since 1911 using archival cartography, multi-temporal aerial photogrammetry and LiDAR data: Implications for coastline changes in 2100 AD. Remote Sens. 2021, 13, 529. [Google Scholar] [CrossRef]
- Franci, F.; Bitelli, G.; Mandanici, E.; Hadjimitsis, D.; Agapiou, A. Satellite remote sensing and GIS-based multi-criteria analysis for flood hazard mapping. Nat. Hazards 2016, 83, 31–51. [Google Scholar] [CrossRef]
- Caballero, I.; Román, A.; Tovar-Sánchez, A.; Navarro, G. Water quality monitoring with Sentinel-2 and Landsat-8 satellites during the 2021 volcanic eruption in La Palma (Canary Islands). Sci. Total Environ. 2022, 822, 153433. [Google Scholar] [CrossRef]
- Figliomeni, F.G.; Parente, C. Bathymetry from satellite images: A proposal for adapting the band ratio approach to IKONOS data. Appl. Geomat. 2023, 15, 565–581. [Google Scholar] [CrossRef]
- Kim, H.C.; Son, S.; Kim, Y.H.; Khim, J.S.; Nam, J.; Chang, W.K.; Lee, J.-H.; Lee, C.-H.; Ryu, J. Remote sensing and water quality indicators in the Korean West coast: Spatio-temporal structures of MODIS-derived chlorophyll-a and total suspended solids. Mar. Pollut. Bull. 2017, 121, 425–434. [Google Scholar] [CrossRef] [PubMed]
- Specht, M.; Specht, C.; Lewicka, O.; Makar, A.; Burdziakowski, P.; Dąbrowski, P. Study on the Coastline Evolution in Sopot (2008–2018) Based on Landsat Satellite Imagery. J. Mar. Sci. Eng. 2020, 8, 464. [Google Scholar] [CrossRef]
- Papakonstantinou, A.; Topouzelis, K.; Pavlogeorgatos, G. Coastline zones identification and 3D coastal mapping using UAV spatial data. ISPRS Int. J. Geo-Inf. 2016, 5, 75. [Google Scholar] [CrossRef]
- Costantino, D.; Pepe, M.; Dardanelli, G.; Baiocchi, V. Using optical Satellite and aerial imagery for automatic coastline mapping. Geogr. Tech. 2020, 15, 171–190. [Google Scholar] [CrossRef]
- Huang, C.; Chen, Y.; Zhang, S.; Wu, J. Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Rev. Geophys. 2018, 56, 333–360. [Google Scholar] [CrossRef]
- McFeeters, S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 1996, 17, 1425–1432. [Google Scholar] [CrossRef]
- Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [Google Scholar] [CrossRef]
- Feyisa, G.L.; Meilby, H.; Fensholt, R.; Proud, S.R. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sens. Environ. 2014, 140, 23–35. [Google Scholar] [CrossRef]
- Fisher, A.; Flood, N.; Danaher, T. Comparing Landsat water index methods for automated water classification in eastern Australia. Remote Sens. Environ. 2016, 175, 167–182. [Google Scholar] [CrossRef]
- Maglione, P.; Parente, C.; Santamaria, R.; Vallario, A. 3D thematic models of land cover from DTM and high-resolution remote sensing images WorldView-2. Rend. Online Soc. Geol. Ital. 2014, 30, 33–40. [Google Scholar] [CrossRef]
- Richards, J.A. Supervised Classification Techniques. In Remote Sensing Digital Image Analysis; Springer: Berlin/Heidelberg, Germany, 2013. [Google Scholar] [CrossRef]
- Schowengerdt, R.A. Techniques for Image Processing and Classifications in Remote Sensing; Academic Press: Cambridge, MA, USA, 2012. [Google Scholar]
- Alcaras, E.; Amoroso, P.P.; Parente, C.; Prezioso, G. Remotely Sensed Image Fast Classification and Smart Thematic Map Production. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2021, 46, 43–50. [Google Scholar] [CrossRef]
- Al-Doski, J.; Mansorl, S.B.; Shafri, H.Z.M. Image classification in remote sensing. J. Environ. Earth Sci. 2013, 3, 140–147. [Google Scholar]
- Hadjitodorov, S.T.; Kuncheva, L.I.; Todorova, L.P. Moderate diversity for better cluster ensembles. Inf. Fusion 2006, 7, 264–275. [Google Scholar] [CrossRef]
- Latini, D.; Del Frate, F.; Palazzo, F.; Minchella, A. Coastline extraction from SAR COSMO-SkyMed data using a new neural network algorithm. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 5975–5977. [Google Scholar] [CrossRef]
- Ebaid, H.M.; Fawzy, H.E.D.; El Shouny, A.F. Automatic Coastline Extraction Using Satellite Images. IOSR J. Mech. Civ. Eng. 2015, 12, 81–86. [Google Scholar] [CrossRef]
- Saeed, A.M.; Fatima, A.M. Coastline extraction using satellite imagery and image processing techniques. Red 2016, 600, 720. [Google Scholar]
- Mirsane, H.; Maghsoudi, Y.; Emadi, R.; Mostafavi, M. Automatic Coastline Extraction Using Radar and Optical Satellite Imagery and Wavelet-IHS Fusion Method. Int. J. Coast. Offshore Eng. 2018, 2, 11–20. [Google Scholar] [CrossRef]
- Dai, C.; Howat, I.M.; Larour, E.; Husby, E. Coastline extraction from repeat high resolution satellite imagery. Remote Sens. Environ. 2019, 229, 260–270. [Google Scholar] [CrossRef]
- Yang, T.; Jiang, S.; Hong, Z.; Zhang, Y.; Han, Y.; Zhou, R.; Wang, J.; Yang, S.; Tong, X.; Kuc, T.Y. Sea-land segmentation using deep learning techniques for landsat-8 OLI imagery. Mar. Geod. 2020, 43, 105–133. [Google Scholar] [CrossRef]
- Domazetović, F.; Šiljeg, A.; Marić, I.; Faričić, J.; Vassilakis, E.; Panđa, L. Automated Coastline Extraction Using the Very High Resolution WorldView (WV) Satellite Imagery and Developed Coastline Extraction Tool (CET). Appl. Sci. 2021, 11, 9482. [Google Scholar] [CrossRef]
- Aghdami-Nia, M.; Shah-Hosseini, R.; Rostami, A.; Homayouni, S. Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net. Int. J. Appl. Earth Obs. Geoinf. 2022, 109, 102785. [Google Scholar] [CrossRef]
- Uddin, M.P.; Mamun, M.A.; Hossain, M.A. PCA-based feature reduction for hyperspectral remote sensing image classification. IETE Tech. Rev. 2021, 38, 377–396. [Google Scholar] [CrossRef]
- Shah, V.P.; Younan, N.H.; King, R.L. An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Trans. Geosci. Remote Sens. 2008, 46, 1323–1335. [Google Scholar] [CrossRef]
- Tochamnanvita, T.; Muttitanon, W. Investigation of coastline changes in three provinces of Thailand using remote sensing. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2014, 40, 1079. [Google Scholar] [CrossRef]
- Rokni, K.; Ahmad, A.; Solaimani, K.; Hazini, S. A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 226–234. [Google Scholar] [CrossRef]
- Paz-Delgado, M.V.; Payo, A.; Gómez-Pazo, A.; Beck, A.L.; Savastano, S. Shoreline Change from Optical and Sar Satellite Imagery at Macro-Tidal Estuarine, Cliffed Open-Coast and Gravel Pocket-Beach Environments. J. Mar. Sci. Eng. 2022, 10, 561. [Google Scholar] [CrossRef]
- Liu, Y.; Wang, X.; Ling, F.; Xu, S.; Wang, C. Analysis of coastline extraction from Landsat-8 OLI imagery. Water 2017, 9, 816. [Google Scholar] [CrossRef]
- Arslan, O.; Akyürek, Ö.; Kaya, Ş.; Şeker, D.Z. Dimension reduction methods applied to coastline extraction on hyperspectral imagery. Geocarto Int. 2020, 35, 376–390. [Google Scholar] [CrossRef]
- Ascione, A.; Aucelli, P.P.; Cinque, A.; Di Paola, G.; Mattei, G.; Ruello, M.; Russo Ermolli, E.; Santangelo, N.; Valente, E. Geomorphology of Naples and the Campi Flegrei: Human and natural landscapes in a restless land. J. Maps 2021, 17, 18–28. [Google Scholar] [CrossRef]
- Budillon, F.; Amodio, S.; Contestabile, P.; Alberico, I.; Innangi, S.; Molisso, F. The present-day nearshore submarine depositional terraces off the Campania coast (South-eastern Tyrrhenian Sea): An analysis of their morpho-bathymetric variability. In Proceedings of the IMEKO TC-19—Proceedings of the International Workshop on Metrology for the Sea, Naples, Italy, 5–7 October 2020; pp. 132–138. [Google Scholar]
- Pusceddu, N.; Batzella, T.; Kalb, C.; Ferraro, F.; Ibba, A.; De Muro, S. Short-term evolution of the Budoni beach on NE Sardinia (Italy). Rend. Online Della Soc. Geol. Ital. 2011, 17, 155–159. [Google Scholar] [CrossRef]
- Melis, R.T.; Di Rita, F.; French, C.; Marriner, N.; Montis, F.; Serreli, G.; Sulas, F.; Vacchi, M. 8000 years of coastal changes on a western Mediterranean island: A multiproxy approach from the Posada plain of Sardinia. Mar. Geol. 2018, 403, 93–108. [Google Scholar] [CrossRef]
- Simeone, S.; De Falco, G. Posidonia oceanica banquette removal: Sedimentological, geomorphological and ecological implications. J. Coast. Res. 2013, 65, 1045–1050. [Google Scholar] [CrossRef]
- Manno, G.; Re, C.L.; Ciraolo, G.; Maltese, A. Coupling a hydro-maritime model and remotely sensed techniques to assess the shoreline positioning uncertainty: The Marsala coast study case. In Proceedings of the Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, Toulouse, France, 20–23 September 2010; SPIE: Bellingham, WA, USA, 2010; Volume 7824, pp. 396–403. [Google Scholar] [CrossRef]
- Martorana, R.; Lombardo, L.; Messina, N.; Luzio, D. Integrated geophysical survey for 3D modelling of a coastal aquifer polluted by seawater. Near Surf. Geophys. 2014, 12, 45–59. [Google Scholar] [CrossRef]
- NASA—USGS. Landsat 9. 2022. Available online: https://landsat.gsfc.nasa.gov/satellites/landsat-9/ (accessed on 8 January 2024).
- Markham, B.L.; Jenstrom, D.; Masek, J.G.; Dabney, P.; Pedelty, J.A.; Barsi, J.A.; Montanaro, M. Landsat 9: Status and plans. In Proceedings of the Earth Observing Systems XXI, San Diego, CA, USA, 30 August–1 September 2016; SPIE: Bellingham, WA, USA, 2016; Volume 9972, pp. 127–132. [Google Scholar] [CrossRef]
- Copernicus Open Access Hub. 2023. Available online: https://scihub.copernicus.eu/ (accessed on 13 January 2023).
- Sentinel-2 User Handbook, ESA. 2015. Available online: https://sentinels.copernicus.eu/documents/247904/685211/Sentinel-2_User_Handbook (accessed on 13 January 2023).
- Hasan, B.M.S.; Abdulazeez, A.M. A review of principal component analysis algorithm for dimensionality reduction. J. Soft Comput. Data Min. 2021, 2, 20–30. [Google Scholar] [CrossRef]
- Roessner, U.; Nahid, A.; Chapman, B.; Hunter, A.; Bellgard, M. Metabolomics—The Combination of Analytical Biochemistry, Biology, and Informatics; Academic Press: Cambridge, MA, USA, 2011. [Google Scholar] [CrossRef]
- Jolliffe, I.T. Principal Component Analysis for Special Types of Data; Springer: New York, NY, USA, 2002; pp. 338–372. [Google Scholar] [CrossRef]
- Shaukat, S.S.; Rao, T.A.; Khan, M.A. Impact of sample size on principal component analysis ordination of an environmental data set: Effects on eigenstructure. Ekológia 2016, 35, 173. [Google Scholar] [CrossRef]
- Beattie, J.R.; Esmonde-White, F.W. Exploration of principal component analysis: Deriving principal component analysis visually using spectra. Appl. Spectrosc. 2021, 75, 361–375. [Google Scholar] [CrossRef] [PubMed]
- Estornell, J.; Martí-Gavilá, J.M.; Sebastiá, M.T.; Mengual, J. Principal component analysis applied to remote sensing. Model. Sci. Educ. Learn. 2013, 6, 83–89. [Google Scholar] [CrossRef]
- Ready, P.; Wintz, P. Information extraction, SNR improvement, and data compression in multispectral imagery. IEEE Trans. Commun. 1973, 21, 1123–1131. [Google Scholar] [CrossRef]
- Omran, M.G.; Engelbrecht, A.P.; Salman, A. An overview of clustering methods. Intell. Data Anal. 2007, 11, 583–605. [Google Scholar] [CrossRef]
- Hidayat, N.; Wardoyo, R.; Azhari, S.N.; Surjono, H.D. Enhanced performance of the automatic learning style detection model using a combination of modified K-means algorithm and naive bayesian. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 638–648. [Google Scholar] [CrossRef]
- Alcaras, E.; Amoroso, P.P.; Figliomeni, F.G.; Parente, C.; Vallario, A. Machine Learning Approaches for Coastline Extraction from Sentinel-2 Images: K-means and K-Nearest Neighbour Algorithms in Comparison. In Proceedings of the Italian Conference on Geomatics and Geospatial Technologies, Genova, Italy, 20–24 June 2022; Springer International Publishing: Cham, Switzerland, 2022; pp. 368–379. [Google Scholar] [CrossRef]
- Modava, M.; Akbarizadeh, G.; Soroosh, M. Hierarchical coastline detection in SAR images based on spectral-textural features and global–local information. IET Radar Sonar Navig. 2019, 13, 2183–2195. [Google Scholar] [CrossRef]
- Zhang, Y.; Qiao, Q.; Liu, J.; Sang, H.; Yang, D.; Zhai, L.; Ning, L.; Yuan, X. Coastline changes in mainland China from 2000 to 2015. Int. J. Image Data Fusion 2022, 13, 95–112. [Google Scholar] [CrossRef]
- Aguilar, F.J.; Fernández, I.; Pérez, J.L.; López, A.; Aguilar, M.A.; Mozas, A.; Cardenal, J. Preliminary results on high accuracy estimation of shoreline change rate based on coastal elevation models. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2010, 33, 986–991. [Google Scholar]
- Alcaras, E.; Falchi, U.; Parente, C.; Vallario, A. Accuracy evaluation for coastline extraction from Pléiades imagery based on NDWI and IHS pan-sharpening application. Appl. Geomat. 2023, 15, 595–605. [Google Scholar] [CrossRef]
- Stehman, S.V.; Wickham, J.D. Pixels, blocks of pixels, and polygons: Choosing a spatial unit for thematic accuracy assessment. Remote Sens. Environ. 2011, 115, 3044–3055. [Google Scholar] [CrossRef]
- Nasr, M.; Zenati, H.; Dhieb, M. Using RS and GIS to mapping land cover of the Cap Bon (Tunisia). In Environmental Remote Sensing and GIS in Tunisia; Springer: Cham, Switzerland, 2021; pp. 117–142. [Google Scholar] [CrossRef]
- Story, M.; Congalton, R.G. Accuracy assessment: A user’s perspective. Photogramm. Eng. Remote Sens. 1986, 52, 397–399. [Google Scholar]
- Dibs, H.; Hasab, H.A.; Al-Rifaie, J.K.; Al-Ansari, N. An optimal approach for land-use/land-cover mapping by integration and fusion of multispectral landsat OLI images: Case study in Baghdad, Iraq. Water Air Soil Pollut. 2020, 231, 488. [Google Scholar] [CrossRef]
- Liu, C.; Frazier, P.; Kumar, L. Comparative assessment of the measures of thematic classification accuracy. Remote Sens. Environ. 2007, 107, 606–616. [Google Scholar] [CrossRef]
- Fung, T.; LeDrew, E. For change detection using various accuracy. Photogramm. Eng. Remote Sens. 1988, 54, 1449–1454. [Google Scholar]
- Comber, A.J. Geographically weighted methods for estimating local surfaces of overall, user and producer accuracies. Remote Sens. Lett. 2013, 4, 373–380. [Google Scholar] [CrossRef]
- Qu, W.; Lu, J.; Li, L.; Li, X. Research on automatic extraction of water bodies and wetlands on HJ satellite CCD images. Remote Sens. Inf. 2011, 4, 28–33. [Google Scholar]
- Alcaras, E.; Amoroso, P.P.; Figliomeni, F.G.; Parente, C.; Prezioso, G. Accuracy Evaluation of Coastline Extraction Methods in Remote Sensing: A Smart Procedure for Sentinel-2 Images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2022, 48, 13–19. [Google Scholar] [CrossRef]
- Li, H.; Wang, X.; Dai, S.; Tian, G. Flood monitoring in Hainan Island based on HJ-CCD data. Trans. Chin. Soc. Agric. Eng. 2015, 31, 191–198. [Google Scholar]
- Alcaras, E.; Amoroso, P.P.; Baiocchi, V.; Falchi, U.; Parente, C. Unsupervised classification based approach for coastline extraction from Sentinel-2 imagery. In Proceedings of the 2021 International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea), Reggio Calabria, Italy, 4–6 October 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 423–427. [Google Scholar] [CrossRef]
- Yang, H.; Wang, Z.; Zhao, H.; Guo, Y. Water body extraction methods study based on RS and GIS. Procedia Environ. Sci. 2011, 10, 2619–2624. [Google Scholar] [CrossRef]
- Mondejar, J.P.; Tongco, A.F. Near infrared band of Landsat 8 as water index: A case study around Cordova and Lapu-Lapu City, Cebu, Philippines. Sustain. Environ. Res. 2019, 29, 16. [Google Scholar] [CrossRef]
Bands | Wavelength (μm) | Resolution (m) |
---|---|---|
L1—Coastal aerosol | 0.43–0.45 | 30 |
L2—Blue | 0.45–0.51 | 30 |
L3—Green | 0.53–0.59 | 30 |
L4—Red | 0.64–0.67 | 30 |
L5—Near Infrared (NIR) | 0.85–0.88 | 30 |
L6—Short-Wave Infrared (SWIR-1) | 1.57–1.65 | 30 |
L7—Short-Wave Infrared (SWIR-2) | 2.11–2.29 | 30 |
Bands | Central Wavelength (µm) | Resolution (m) |
---|---|---|
B1—Coastal Aerosol | 0.443 | 60 |
B2—Blue | 0.490 | 10 |
B3—Green | 0.560 | 10 |
B4—Red | 0.665 | 10 |
B5—Red Edge1 | 0.705 | 20 |
B6—Red Edge2 | 0.740 | 20 |
B7—Red Edge3 | 0.783 | 20 |
B8—NIR | 0.842 | 10 |
B8A—Narrow NIR | 0.865 | 20 |
B9—Water Vapor | 0.945 | 60 |
B10—SWIR Cirrus | 1.375 | 60 |
B11—SWIR-1 | 1.610 | 20 |
B12—SWIR-2 | 2.190 | 20 |
Dataset | Area | Coefficients | Percentage of Variance—First Component |
---|---|---|---|
Landsat | Campania | (0.1316, 0.1501, 0.2211, 0.2714, 0.6645, 0.5159, 0.3604) | 90.29% |
Sentinel 20 m | (0.0835, 0.1438, 0.1842, 0.2549, 0.4014, 0.4552, 0.4909, 0.4177, 0.2995) | 90.81% | |
Sentinel 10 m | (0.1610, 0.2721, 0.3419, 0.8849) | 85.37% | |
Landsat | Sardinia | (0.0654, 0.0930, 0.1859, 0.2438, 0.6395, 0.5899, 0.3691) | 94.47% |
Sentinel 20 m | (0.0341, 0.1076, 0.1349, 0.2240, 0.4003, 0.4553, 0.5016, 0.4587, 0.2990) | 92.60% | |
Sentinel 10 m | (0.0829, 0.2351, 0.3149, 0.9158) | 90.59% | |
Landsat | Sicily | (0.1173, 0.1367, 0.2202, 0.3051, 0.5370, 0.5941, 0.4298) | 96.86% |
Sentinel 20 m | (0.0908, 0.1623, 0.2364, 0.2883, 0.3463, 0.3829, 0.4161, 0.4865, 0.3873) | 95.83% | |
Sentinel 10 m | (0.1972, 0.3376, 0.4786, 0.7862) | 92.59% |
Method | Min (m) | Max (m) | Mean (m) | Dev.ST. (m) | RMSE (m) |
---|---|---|---|---|---|
PCA-Campania | 0 | 34.170 | 9.431 | 7.580 | 12.100 |
NDWI-Campania | 0 | 50.574 | 10.369 | 9.948 | 14.369 |
MNDWI-Campania | 0.047 | 48.075 | 11.067 | 11.694 | 16.101 |
PCA-Sardinia | 0 | 30.011 | 7.751 | 4.540 | 8.983 |
NDWI-Sardinia | 0.078 | 35.575 | 10.087 | 7.365 | 12.490 |
MNDWI-Sardinia | 0 | 44.2 | 9.519 | 7.057 | 11.850 |
PCA-Sicily | 0 | 29.993 | 7.710 | 4.374 | 8.864 |
NDWI-Sicily | 0.174 | 74.898 | 9.659 | 7.974 | 12.525 |
MNDWI-Sicily | 0.068 | 66.988 | 9.186 | 6.889 | 11.482 |
Method | Min (m) | Max (m) | Mean (m) | Dev.ST. (m) | RMSE (m) |
---|---|---|---|---|---|
PCA-Campania | 0 | 19.569 | 4.472 | 2.829 | 5.292 |
NDWI-Campania | 0 | 25.842 | 5.422 | 5.397 | 7.650 |
MNDWI-Campania | 0 | 26.044 | 6.980 | 5.838 | 9.100 |
PCA-Sardinia | 0 | 19.883 | 4.249 | 2.754 | 5.064 |
NDWI-Sardinia | 0.007 | 34.952 | 4.932 | 4.179 | 6.464 |
MNDWI-Sardinia | 0 | 20.065 | 4.941 | 3.731 | 6.192 |
PCA-Sicily | 0.060 | 23.678 | 4.715 | 2.633 | 5.400 |
NDWI-Sicily | 0.060 | 28.102 | 7.043 | 5.355 | 8.847 |
MNDWI-Sicily | 0.025 | 23.842 | 6.859 | 4.954 | 8.462 |
Method | Min (m) | Max (m) | Mean (m) | Dev.ST. (m) | RMSE (m) |
---|---|---|---|---|---|
PCA-Campania | 0 | 14.427 | 2.470 | 1.565 | 2.924 |
NDWI-Campania | 0 | 20.622 | 3.545 | 3.422 | 4.927 |
MNDWI-Campania | 0 | 20.959 | 3.578 | 3.533 | 5.028 |
PCA-Sardinia | 0 | 18.931 | 2.898 | 2.358 | 3.736 |
NDWI-Sardinia | 0 | 17.544 | 3.269 | 2.762 | 4.280 |
MNDWI-Sardinia | 0 | 17.700 | 3.170 | 2.410 | 3.982 |
PCA-Sicily | 0.056 | 16.080 | 2.614 | 1.537 | 3.032 |
NDWI-Sicily | 0.040 | 20.816 | 3.704 | 3.110 | 4.837 |
MNDWI-Sicily | 0.060 | 20.208 | 3.840 | 3.071 | 4.917 |
Method | Accuracy Index | Water | No-Water |
---|---|---|---|
PCA-Sardinia | UA | 99.41% | 95.23% |
PA | 94.86% | 99.45% | |
OA | 97.19% | ||
NDWI-Sardinia | UA | 99.89% | 91.81% |
PA | 90.80% | 99.90% | |
OA | 95.42% | ||
MNDWI-Sardinia | UA | 99.76% | 94.39% |
PA | 93.88% | 99.78% | |
OA | 96.88% | ||
PCA-Campania | UA | 99.98% | 90.98% |
PA | 89.57% | 99.98% | |
OA | 94.91% | ||
NDWI-Campania | UA | 99.54% | 89.61% |
PA | 87.83% | 99.61% | |
OA | 93.87% | ||
MNDWI-Campania | UA | 99.71% | 85.82% |
PA | 82.64% | 99.77% | |
OA | 91.43% | ||
PCA-Sicily | UA | 99.45% | 98.88% |
PA | 98.79% | 99.49% | |
OA | 99.15% | ||
NDWI-Sicily | UA | 99.46% | 95.17% |
PA | 94.57% | 99.52% | |
OA | 97.14% | ||
MNDWI-Sicily | UA | 99.35% | 97.56% |
PA | 97.33% | 99.41% | |
OA | 98.41% |
Method | Accuracy Index | Water | No-Water |
---|---|---|---|
PCA-Sardinia | UA | 99.56% | 99.19% |
PA | 99.25% | 99.52% | |
OA | 99.38% | ||
NDWI-Sardinia | UA | 99.40% | 95.21% |
PA | 95.73% | 99.32% | |
OA | 97.39% | ||
MNDWI-Sardinia | UA | 97.67% | 98.40% |
PA | 98.44% | 97.61% | |
OA | 98.03% | ||
PCA-Campania | UA | 99.71% | 98.34% |
PA | 98.17% | 99.74% | |
OA | 98.99% | ||
NDWI-Campania | UA | 96.97% | 98.41% |
PA | 98.29% | 97.18% | |
OA | 97.71% | ||
MNDWI-Campania | UA | 96.44% | 99.37% |
PA | 99.33% | 96.63% | |
OA | 97.92% | ||
PCA-Sicily | UA | 99.72% | 98.62% |
PA | 98.46% | 99.75% | |
OA | 99.14% | ||
NDWI-Sicily | UA | 98.32% | 97.65% |
PA | 97.38% | 98.49% | |
OA | 97.96% | ||
MNDWI-Sicily | UA | 97.64% | 98.71% |
PA | 98.59% | 97.84% | |
OA | 98.20% |
Method | Accuracy Index | Water | No-Water |
---|---|---|---|
PCA-Campania | UA | 99.92% | 97.37% |
PA | 97.06% | 99.92% | |
OA | 98.55% | ||
NDWI-Campania | UA | 98.30% | 98.56% |
PA | 98.44% | 98.43% | |
OA | 98.44% | ||
MNDWI-Campania | UA | 97.81% | 99.23% |
PA | 99.17% | 97.95% | |
OA | 98.54% | ||
PCA-Sardinia | UA | 99.10% | 98.87% |
PA | 98.95% | 99.03% | |
OA | 98.99% | ||
NDWI-Sardinia | UA | 99.50% | 97.48% |
PA | 97.70% | 99.45% | |
OA | 98.52% | ||
MNDWI-Sardinia | UA | 97.07% | 99.91% |
PA | 99.91% | 97.04% | |
OA | 98.45% | ||
PCA-Sicily | UA | 99.72% | 98.61% |
PA | 98.45% | 99.75% | |
OA | 99.13% | ||
NDWI-Sicily | UA | 98.93% | 97.16% |
PA | 96.81% | 99.05% | |
OA | 97.98% | ||
MNDWI-Sicily | UA | 98.24% | 98.61% |
PA | 98.47% | 98.40% | |
OA | 98.43% |
Dataset | Min (m) | Max (m) | Mean (m) | Dev.ST. (m) | RMSE (m) |
---|---|---|---|---|---|
Landsat L5 | 0.000 | 48.140 | 8.259 | 5.346 | 9.839 |
Landsat L5-L6-L7 | 0.000 | 30.011 | 7.938 | 4.906 | 9.332 |
Sentinel 20 m (B8A) | 0.000 | 22.871 | 5.200 | 3.161 | 6.085 |
Sentinel 20 m (B5, B6, B7, B8A, B11, B12) | 0.000 | 16.805 | 4.612 | 2.744 | 5.367 |
Sentinel 10 m (B8) | 0.000 | 23.558 | 3.235 | 2.516 | 4.098 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Parente, C.; Alcaras, E.; Figliomeni, F.G. Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach. Remote Sens. 2024, 16, 1817. https://doi.org/10.3390/rs16101817
Parente C, Alcaras E, Figliomeni FG. Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach. Remote Sensing. 2024; 16(10):1817. https://doi.org/10.3390/rs16101817
Chicago/Turabian StyleParente, Claudio, Emanuele Alcaras, and Francesco Giuseppe Figliomeni. 2024. "Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach" Remote Sensing 16, no. 10: 1817. https://doi.org/10.3390/rs16101817