A hybrid particle swarm optimization algorithm for coastline SAR image automatic detection

Fan, Jianchao; Cao, Ke; Zhao, Jun; Jiang, Dawei; Tang, Xiaoliang

doi:10.1109/wcica.2016.7578256

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Cao et al (2016) proposed a new geometric active contour model for waterline detection from SAR images, which is adaptive to the speckle noises. Fan et al (2016) proposed a level set approach with a particle swarm optimization algorithm for waterline automatic detection in SAR images. Elkhateeb et al (2021) adopted a modified Chan-Vese method for sea-land segmentation, which is initiated by a superpixel-based fuzzy c-means automatically.…”

Section: Introductionmentioning

confidence: 99%

Waterline Extraction for Artificial Coast With Vision Transformers

Yang

Wang

Zhai

2022

Front. Environ. Sci.

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Accurate acquisition for the positions of the waterlines plays a critical role in coastline extraction. However, waterline extraction from high-resolution images is a very challenging task because it is easily influenced by the complex background. To fulfill the task, two types of vision transformers, segmentation transformers (SETR) and semantic segmentation transformers (SegFormer), are introduced as an early exploration of the potential of transformers for waterline extraction. To estimate the effects of the two methods, we collect the high-resolution images from the web map services, and the annotations are created manually for training and test. Through extensive experiments, transformer-based approaches achieved state-of-the-art performances for waterline extraction in the artificial coast.

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Section: Introductionmentioning

confidence: 99%

Waterline Extraction for Artificial Coast With Vision Transformers

Yang

Wang

Zhai

2022

Front. Environ. Sci.

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“…research direction of scholars at home and abroad because of its high efficiency and reusability. At present, the methods of coastline automatic extraction mainly include threshold segmentation-based method [3], index analysis-based method [4], [5], active contour model-based method [6], [7], region growing-based method [8]- [10], etc. These methods usually rely on the feature knowledge of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Coastline Extraction Method Based on Convolutional Neural Networks—A Case Study of Jiaozhou Bay in Qingdao, China

Jia

Liu

et al. 2019

IEEE Access

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The traditional edge detection-based shoreline extraction method is severely disturbed by noise, and it is difficult to obtain a continuous coastline. In response to the above problems, we propose a coastline extraction method based on convolutional neural networks. Firstly, we replace the standard convolution with the Mini-Inception structure in the backbone network to extract multi-scale features of the object, and all the multi-scale features are concatenated. Then, we use the leaky-ReLU activation function instead of the ReLU activation function to avoid the problem that ''dead'' neurons cannot learn the effective features of remote sensing images. Finally, the network fully exploits multi-level information of objects to perform the image-to-image prediction. We carried out experiments on the remote sensing images of Jiaozhou Bay in Qingdao. The experimental results showed that our method could effectively extract the coastline automatically, and the producer's accuracy and the user's accuracy were higher than the comparison methods. INDEX TERMS Coastline extraction, remote sensing images, convolutional neural networks, backbone networks, activation function.

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