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

Author:

Song Derui12,Zhu Cheng2,Tao Jingzhe2,Shi Xiaofei3,Wang Xianghai14ORCID

Affiliation:

1. School of Geographical Sciences, Liaoning Normal University, Dalian 116029, China

2. National Marine Environmental Monitoring Center, Dalian 116023, China

3. Information Science and Technology College, Dalian Maritime University, Dalian 116026, China

4. School of Computer Science and Artificial Intelligence, Liaoning Normal University, Dalian 116081, China

Abstract

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.

Funder

National Key R & D Program of China, Ministry of Science and Technology of P.R.C.

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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