An OBIA and Rule Algorithm for Coastline Extraction from High- and Medium-Resolution Multispectral Remote Sensing Images

Sreekesh, S.; Kaur, Navneet; Naik, S. R. Sreerama

doi:10.1007/s41976-020-00032-z

Cited by 9 publications

(3 citation statements)

References 36 publications

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“…The polygon features were converted to polylines which were then smoothed to improve their cartographic quality. This improvement was achieved using a polynomial approximation with exponential kernel (PAEK) algorithm which calculated a smooth line passing through the input vertices of the polylines with a smoothing tolerance of 40 m [70,71]. Lower levels of smoothing tolerance resulted in unacceptably jagged lines.…”

Section: Raster-featured Cum Vector-based Analysismentioning

confidence: 99%

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Dike,

Oyetunji,

Amaechi

2023

JMSE

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Satellite image analysis is a potentially powerful tool for monitoring coastal shoreline positions. This study explores the use of multi-temporal, dual-polarised Sentinel-1 GRD synthetic aperture radar (SAR) imagery with a spatial resolution of 10 m for delineating shorelines. It was conducted in a data-deficient and complex environment (the Niger delta of Nigeria), in a developing country with a cloud-heavy climate. The study focuses on exploring and testing the capability of using multitemporal waterlines from SAR images to derive shoreline positions at high and low tidal states. From 54 Sentinel-1 images recorded in 2017, the study selected 12 images to represent both high and low tidal states. These were spread across the wet and dry seasons in order to account for seasonal differences. Shoreline positions were obtained by identifying the land–water boundary via segmentation using histogram-minimum thresholding, vectorizing and smoothing that boundary, and averaging its position over multiple waterlines. The land–water segmentation had an overall accuracy of 95–99%. It showed differences between wet and dry season shoreline positions in areas dominated by complex creek networks, but similarities along open coasts. The SAR-derived shorelines deviated from the reference lines by a maximum of 43 m (approximately four pixels), and often less than 10 m (one pixel) in most locations (open coast, estuarine, complex creek networks) at high and low tides, except low tide lines in areas with extensive inter-tidal flats at shorelines 70 m to 370 m from the reference lines. However, for applications such as coastal vulnerability assessment, the high tide shoreline is of greater importance. Thus, depending on the application of interest, problems with low tide shoreline delineation may be irrelevant. Despite limitations, notably the relatively small number of images available that were recorded at high or low tide, the method provides a simple, objective, and cost-effective approach to monitoring shorelines at high and low tide.

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Section: Raster-featured Cum Vector-based Analysismentioning

confidence: 99%

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Dike,

Oyetunji,

Amaechi

2023

JMSE

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“…Indeed, most of the research about coastal ecosystem mapping and monitoring supported by UAV technology refers to underwater coastal ecosystems (Monteiro et al., 2021; Ventura et al., 2018). Another methodological tool developed by the UAV technologies was the landscape metrics analysis (LMA) using the object‐based image analysis (OBIA) approach possible due to the very high spatial resolution of drone (Frohn, 2018; Sreekesh et al., 2020). Landscape metrics analysis makes use of algorithms to measure units of landscape composition, in order to quantify number, quantity, and area of cover patch types and the spatial structure of patterns within a geographic area.…”

Section: Introductionmentioning

confidence: 99%

Modeling approach for coastal dune habitat detection on coastal ecosystems combining very high‐resolution UAV imagery and field survey

Agrillo

Filipponi

Salvati³

et al. 2023

Remote Sens Ecol Conserv

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Earth observation (EO) data, derived from remote sensing and unmanned aerial vehicle (UAV), have been recently demonstrated to be essential tools for the ecosystem monitoring and habitat mapping, combining high technological and methodological procedures for applied ecology. However, research based on EO data analyses often tend to focus on image processing techniques, neglecting the development of a detailed sampling design scheme needed for an exhaustive habitat detection. This paper shows the results of a novel approach for mapping coastal dune habitats at a fine scale, using a supervised machine learning model, through the combination of vegetation plot sampling scheme, synergic use of multi-sensor spectral imagery (UAV-VHR) and environmental predictors (e.g., LiDAR), object-based image analysis, and landscape metrics analysis. Proposed approach was tested in a protected area, established to preserve notable habitats along the Italian Tyrrhenian coast. A detailed sampling scheme was designed and carried out during spring and summer of 2019, combining simultaneously UAV flight acquisition and field vegetation survey data, collected at high precision positioning. The calibrated classification model achieved an overall accuracy of 78.6% (standard error 4.33), allowing us to accurately classify and map five coastal habitats, according to EUNIS (European Nature Information System) classification, which were further verified through a fully independent validation field survey. Results demonstrate that VHR imageries, combined with specific field survey schemes, can be exploited to train classification models used for the detection of plant communities (i.e., meso-habitat) and plant species at local scale. Our findings demonstrate that UAV-VHR data is a valid tool to produce high spatial resolution information in sand beach ecosystems, giving ecology research a new way for responsive, timely, and cost-effective ecosystem monitoring.

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“…Extracting the coastline using conventional ground exploration techniques is frequently arduous, time-consuming, and occasionally impracticable for comprehensive coverage of the entire coastline system [3]. Due to its convenient image acquisition, extensive geographic coverage, abundant image information, and high effectiveness, remote sensing technology is increasingly employed in the study of coastline detection, extraction, and classification [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Shoreline Extraction Method Based on Double-Loop Network Framework

Li,

Cao,

et al. 2023

Preprint

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Satellite remote sensing-based shoreline extraction can provide great supports for scientific planning and utilization of coastal zones, marine ecological protection, and efficient shoreline management. In this paper, based on the designed double-loop network framework, we propose an adaptive image segmentation algorithm to achieve end-to-end shoreline extraction. Specifically, by considering triple constraints, a label reassignment module is firstly designed to enabling the extraction network with self-supervised learning ability for adaptive segmentation. Secondly, a data stream attention scheme is constructed to guide the network’s learning directionally, which could enhance the robustness and convergence speed of the double-loop network. Finally, a series of experiments are carried out to validate the effectiveness and feasibility of our proposed work.

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An OBIA and Rule Algorithm for Coastline Extraction from High- and Medium-Resolution Multispectral Remote Sensing Images

Cited by 9 publications

References 36 publications

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Modeling approach for coastal dune habitat detection on coastal ecosystems combining very high‐resolution UAV imagery and field survey

A Shoreline Extraction Method Based on Double-Loop Network Framework

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