A Superpixel-Based Coastline Extraction Algorithm for Single-Polarized ENVISAT and ERS Imagery

Shi, Xiaofei; Zhu, Cheng; Ding, Xing Hao; Du, Qian; Younan, Nicolas H.; Li, Li

doi:10.1109/jstars.2019.2954619

Cited by 4 publications

(6 citation statements)

References 45 publications

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“…Superpixel-based methods are typical region segmentation methods, grouping pixels into perceptive atomic regions, which can be used to replace the rigid structure of the pixel grid [67]. The simple linear iterative clustering (SLIC) algorithm [67] is one of the most widely used methods due to the advantages of simplicity and efficiency and has been used in the application of SAR images [31]. Therefore, we use SLIC to generate superpixels.…”

Section: Automatic Seed Points Generationmentioning

confidence: 99%

“…If a large image is involved in computation on shoreline extraction, the computation burden and computer capability have to be seriously considered. To reduce this burden, this paper takes a similar trick as other shoreline extraction methods [31]. A downsampling technique has been used.…”

Section: Coarse Extraction Of Shorelinementioning

confidence: 99%

“…In general, multi-polarized SAR images have been successfully applied to shoreline extraction. However, most of the existing methods have limitations in complex sea state conditions [31].…”

Section: Introductionmentioning

confidence: 99%

“…Apart from the above three kinds of data, primarily relying on rich data information, single-polarized SAR images are the easiest and most widely used to extract shoreline. Generally, the shoreline extraction methods of single-polarized SAR images can be divided into several categories, e.g., the edge extraction-based methods [14,32], the thresholdbased methods [33,34], the region merge-based methods [35,36], the partial differential equation-based methods [13,37], the deep learning-based methods [2,38], the Markov random field-based methods [39,40], and superpixels-based methods [31,41]. These existing methods have achieved good results under specific conditions and study areas.…”

Section: Introductionmentioning

confidence: 99%

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DLRW: Dual-Link Weight Random Walk Model for Aquaculture Boundary Extraction by Single-Polarized SAR Imagery

et al. 2023

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Coastal aquaculture is undertaken in shallow and usually sheltered waters along the coast, delineated by aquaculture ponds. Illegal usage of coastal aquaculture can lead to conflicts with local communities and environmental problems. Thus, it is necessary to extract the aquaculture boundary to monitor the expansion of coastal aquaculture to the sea. However, it is challenging for most existing algorithms to extract the aquaculture boundary for synthetic aperture radar (SAR) images under a high incident angle (>30 degree) with horizontal transmitted and received (HH) or vertical transmitted and received (VV) polarization. The difficulties come from the following: (1) seawater can be seen on both sides of such boundaries, (2) the contrast of such boundaries is uneven, and (3) the backscattering coefficients in some parts of such boundaries are low. In this paper, a novel dual-link weight random walk (DLRW)-based method is proposed to extract such boundaries. The proposed DLRW is composed of an automatic seed points generation strategy, and the establishment and solving of a random walk model with the dual-link weight. By a coarse-to-fine procedure, DLRW is used to extract the aquaculture boundaries in the whole imagery. Sentinel-1 and GF-3 images in Dalian and Liaodong Bay, China have been used in experiments. Mean offset (MO), root mean square error (RMSE), Overlapped, accuracy within one pixel (WOP), and accuracy within two pixels (WTP) have been used to evaluate the performance with existing methods. Experimental results have demonstrated the proposed DLRW-based method outperforms existing methods in the extraction on aquaculture boundaries. Under the low tide, the DLRW-based method is better than the other two methods with MO, RMSE, Overlapped, WOP, and WTP by at least 5.75 pixels, 10.43 pixels, 2.88%, 11.09%, and 18.04%, respectively. Under the high tide, the DLRW-based method is superior to the other two methods with MO, RMSE, and WTP by at least 3.8 pixels, 10.5 pixels, and 6.3%. In addition, the proposed DLRW-based method has a good ability to extract the shoreline with bedrock, ports, and silt. Therefore, the proposed DLRW-based method can be of great value to coastal aquaculture monitoring, coastal mapping, and other coastal applications.

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Section: Automatic Seed Points Generationmentioning

confidence: 99%

Section: Coarse Extraction Of Shorelinementioning

confidence: 99%

“…In general, multi-polarized SAR images have been successfully applied to shoreline extraction. However, most of the existing methods have limitations in complex sea state conditions [31].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DLRW: Dual-Link Weight Random Walk Model for Aquaculture Boundary Extraction by Single-Polarized SAR Imagery

et al. 2023

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“…Finally, the coastline is obtained via contour evolution. Shi et al proposed a coastline extraction algorithm by utilising superpixels as the basic unit [14]. An improved line finder was used in the simple linear iterative clustering to enhance results around the dam.…”

Section: Introductionmentioning

confidence: 99%

General Coastline Extraction Based on an Improved Active Contour Model in Jiaozhou Bay, Qingdao, China, from 1990 to 2018 Using Landsat Satellite Images

Jie

Guan

et al. 2021

International Journal of Photoenergy

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The coastline is located at the junction of the sea and the land, and it is essential for ecological environment. However, most existing methods can extract the coastline with obvious boundaries and cannot obtain the general coastline, including an intertidal zone and salt field. Accordingly, a new general coastline extraction method is proposed on the basis of an improved active contour model to extract the general coastline from remote sensing images. An improved active contour model was proposed to extract the water area by introducing aiming energy of water from the Modified Normalized Difference Water Index information. Then, mathematical morphology was applied to obtain the seawater area based on the extracted water area. Finally, the coastline was refined and generated by the improved active contour model in a buffer zone of the seawater boundary. Landsat images over Jiaozhou Bay in Shandong Province, China, from 1990 to 2018 were used to extract the general coastline. Results demonstrate that the proposed method can effectively extract the general coastline, which is close to the reference coastline. The length of the coastline decreased from 234.64 km in 1990 to 221.21 km in 2000. This value significantly increased to 255.05 km from 2000 to 2010. The main reason is that Hongdao Island merged with the mainland due to reclamation. The length of the coastline slightly decreased by approximately 12 km from 2010 to 2018 due to environmental protection measures and the reclamation prohibition.

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A Coastline Detection Algorithm with ACM Driven by Diffusion Coefficient for SAR Imagery

Shi

Zhang

Song

et al. 2021

Lecture Notes in Electrical Engineering

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A Superpixel-Based Coastline Extraction Algorithm for Single-Polarized ENVISAT and ERS Imagery

Cited by 4 publications

References 45 publications

DLRW: Dual-Link Weight Random Walk Model for Aquaculture Boundary Extraction by Single-Polarized SAR Imagery

DLRW: Dual-Link Weight Random Walk Model for Aquaculture Boundary Extraction by Single-Polarized SAR Imagery

General Coastline Extraction Based on an Improved Active Contour Model in Jiaozhou Bay, Qingdao, China, from 1990 to 2018 Using Landsat Satellite Images

A Coastline Detection Algorithm with ACM Driven by Diffusion Coefficient for SAR Imagery

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