A Novel Region-Merging Approach for Coastline Extraction From Sentinel-1A IW Mode SAR Imagery

Liu, Zhongling; Li, Fēi; Li, Ning; Wang, Robert; Zhang, Heng

doi:10.1109/lgrs.2015.2510745

Cited by 50 publications

(25 citation statements)

References 18 publications

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“…There are some methods to extract the coastline information from a radar image. Zhongling et al proposed a method using the combination of K-means and object-based region-merging, while Ferdinando et al proposed dual-polarization scattering model based method [31,32]. These methods have a good performance in extracting the coastline information feature, but these methods need to perform the complex operations which are time taking, hence making these methods unsuitable for real time application.…”

Section: A Problem Descriptionmentioning

confidence: 99%

Radar Image-Based Positioning for USV Under GPS Denial Environment

Smart

Ahmed

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-Unmanned Surface Vehicle (USV) is an important application of unmanned systems and these USVs provide safe and secure operation in hostile environments. But these USVs are highly reliant on their positioning system such as Global Position System (GPS) and loss of positioning information from GPS can cause catastrophe. To overcome this positioning challenge for a USV under GPS denial environment, we propose a real-time positioning algorithm based on radar and satellite images to determine the USV position. The algorithm takes coastline as a registration feature to implement an image registration between a horizontal viewing angle image from a radar and a vertical viewing angle image from a satellite. The contributions of this paper consist of two parts. Firstly, a coastline feature extraction method based on edge gray features for both radar and satellite images is provided. Secondly, a high efficiency image registration method which takes the dimensionality reduction distance as an indicator was proposed for USV embedded system. The results from six typical application scenarios show that the maximum positioning error of the proposed algorithm is 28.02 m under the worst case. A continuous positioning experiment shows that the average error of the algorithm is 9.77m, which indicates that the algorithm can meet the positioning requirements of a USV under GPS denial environment.

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Section: A Problem Descriptionmentioning

confidence: 99%

Radar Image-Based Positioning for USV Under GPS Denial Environment

Smart

Ahmed

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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“…A new automatic algorithm, based on pulse-coupled neural networks (PCNN), has been used to delineate coastlines by processing COSMO-SkyMed SAR products generated from different polarizations, geometric configurations, and measurement modes [10]. A novel algorithm (the modified K-means method and an adaptive coarse-fine object-based region-merging, MLAORM) for coastline extraction has been proposed for wide-swath Sentinel-1A images, which combines the modified K-means method and an adaptive coarse-fine object-based region-merging scheme [11]. More than 93% of the detected coastlines in Indonesia is located within 2-pixels distance in comparison with the manually traced coastlines [11].…”

Section: Introductionmentioning

confidence: 99%

“…A novel algorithm (the modified K-means method and an adaptive coarse-fine object-based region-merging, MLAORM) for coastline extraction has been proposed for wide-swath Sentinel-1A images, which combines the modified K-means method and an adaptive coarse-fine object-based region-merging scheme [11]. More than 93% of the detected coastlines in Indonesia is located within 2-pixels distance in comparison with the manually traced coastlines [11]. Another new metric used to process dual-polarization coherent and incoherent SAR data for coastline extraction purposes allowed discriminating land from sea using an unsupervised classification scheme [12].…”

Section: Introductionmentioning

confidence: 99%

Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update

Zhang

Shokr

et al. 2019

Remote Sensing

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The temporal and spatial variability of the Antarctic coastline is a clear indicator of change in extent and mass balance of ice sheets and shelves. In this study, the Canny edge detector was utilized to automatically extract high-resolution information of the Antarctic coastline for 2005, 2010, and 2017, based on optical and microwave satellite data. In order to improve the accuracy of the extracted coastlines, we developed the Canny algorithm by automatically calculating the local low and high thresholds via the intensity histogram of each image to derive thresholds to distinguish ice sheet from water. A visual comparison between extracted coastlines and mosaics from remote sensing images shows good agreement. In addition, comparing manually extracted coastline, based on prior knowledge, the accuracy of planimetric position of automated extraction is better than two pixels of Landsat images (30 m resolution). Our study shows that the percentage of deviation (<100 m) between automatically and manually extracted coastlines in nine areas around the Antarctica is 92.32%, and the mean deviation is 38.15 m. Our results reveal that the length of coastline around Antarctica increased from 35,114 km in 2005 to 35,281 km in 2010, and again to 35,672 km in 2017. Meanwhile, the total area of the Antarctica varied slightly from 1.3618 × 10 7 km 2 (2005) to 1.3537 × 10 7 km 2 (2010) and 1.3657 × 10 7 km 2 (2017). We have found that the decline of the Antarctic area between 2005 and 2010 is related to the breakup of some individual ice shelves, mainly in the Antarctic Peninsula and off East Antarctica. We present a detailed analysis of the temporal and spatial change of coastline and area change for the six ice shelves that exhibited the largest change in the last decade. The largest area change (a loss of 4836 km 2 ) occurred at the Wilkins Ice Shelf between 2005 and 2010.

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“…Paes et al (2015) proposed a method for detect shoreline from RadarSAT-2 SAR data by using k-means clustering algorithm and Canny edge detection filter. Liu et al (2016) improved a method for coastline extraction for wide-swath SAR imagery, which used the modified K-means method and an adaptive coarse-fine object-based region-merging (MKAORM). Modava and Akbarizadeh (2017) presented a novel coastline extraction procedure from SAR images utilizing spatial fuzzy clustering and the active contour method.…”

Section: Introductionmentioning

confidence: 99%

Integrated Shoreline Extraction Approach With Use of Rasat MS and Sentinel-1a Sar Images

Demir

Erdem

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of AntalyaKemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.

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A Novel Region-Merging Approach for Coastline Extraction From Sentinel-1A IW Mode SAR Imagery

Cited by 50 publications

References 18 publications

Radar Image-Based Positioning for USV Under GPS Denial Environment

Radar Image-Based Positioning for USV Under GPS Denial Environment

Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update

Integrated Shoreline Extraction Approach With Use of Rasat MS and Sentinel-1a Sar Images

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