The study of coastline extraction of the Pearl River Estuary based on ETM+ remote sensing image

Wang, Ping; Wan, Yangchun; Fu-zhou, Duan

doi:10.1201/b19126-193

Cited by 3 publications

(4 citation statements)

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“…Nowadays, automatic coastline extraction based on the computer is more and more popular. Soble algorithm and MNDWI method were used in extracting coastline of the Yellow River Delta based on Landsat image [2] .MNDW is suggested based on NDWI [3] .Mathematical expression of MNDWI [4] is shown in Eq. 1.…”

Section: Methodsmentioning

confidence: 99%

Yellow River Estuary Coastline Change Analysis before and after the Water and Sediment Regulation in 12 Years

Yang

Wei

2014

AMR

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Based on the Landsat TM images of the Yellow River Delta from 2002 to 2013, the coastlines before and after water and sediment regulation in each year were extracted by the Normalized Difference Water Index (NDWI) with RS and GIS software. The dynamic changes of coastlines were analyzed in combination with the conditions of water and sediment regulation. The results show that the coastlines were generally pushed ahead though it changed ahead and back alternately in the past decade. The coastline near the estuary changed more obviously than other area. There is a positive linear relationship between the rate of the coastline pushing ahead and the ratio of the water and sediment quantity discharged into the sea in this region.

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Section: Methodsmentioning

confidence: 99%

Yellow River Estuary Coastline Change Analysis before and after the Water and Sediment Regulation in 12 Years

Yang

Wei

2014

AMR

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“…Dianchi Lake is the only one to receive pollutions in the lake area in Kunming. What's more, it is located in the downstream of Kunming, which has a population of 2.20×10 6 , with all wastewater from Kunming city injected into Dianchi Lake, which is the root cause of Dianchi Lake seriously polluted. Dianchi Lake is a national key environmental governance "three rivers and three lakes, the waters", rapid, accurate extraction of the water boundary can analyze the trend of Dianchi Lake, providing technical support for comprehensive management of Dianchi Lake and supervision, having a vital significance.…”

Section: Study Areamentioning

confidence: 99%

“…Although it can effectively extract water information , but still mixed with non water information; Gudina L et al improved the distinction between shadow and the accuracy of the other places through a new automatic water extraction method, using MNDW each pixel and the stability of the sub-pixel threshold [5] . Lijuan Wang et al have done the extraction and verification researches on the coastline [6] and so on.…”

Section: Introductionmentioning

confidence: 99%

A Fast Extraction Method of Water Boundary Based on the Landsat TM Image

Wang

Wen

Zhang

et al. 2014

AMM

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The extraction of water boundary is the basis for measurement of the watershed area and the extraction of other water parameters, which has an important impact on calculation accuracy of hydrological analysis. With the rapid development of remote sensing technology, the extraction of water system and basin boundary is getting more and more importance and the extraction methods have also made a new progress. This paper takes the acquisitive TM image of Dianchi Lake of 1992. Firstly the image is enhanced by Tassled Cap Transformation, and then band ratio (b3/b1). By setting appropriate threshold to the enhanced image, we can extract the water boundary more quickly and effectively, and have achieved good results.

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“…The process is time-consuming and laborious, and it is difficult to determine the parameters. However, the edge detection-based method [11]- [14] can detect the positions of the step change of gray value by the spatial relationship of coastline with Roberts, Canny, Sobel, Prewitt, Log, and other operators. The method is simple and effective and does not need to rely on the feature knowledge of remote sensing images to determine the parameters, so the method based on edge detection becomes the main method of coastline automatic extraction.…”

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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The study of coastline extraction of the Pearl River Estuary based on ETM+ remote sensing image

Cited by 3 publications

References 0 publications

Yellow River Estuary Coastline Change Analysis before and after the Water and Sediment Regulation in 12 Years

Yellow River Estuary Coastline Change Analysis before and after the Water and Sediment Regulation in 12 Years

A Fast Extraction Method of Water Boundary Based on the Landsat TM Image

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

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