基于改进的深度学习网络的SAR图像瞬时海岸线自动提取算法

doi:10.12305/j.issn.1001-506X.2021.08.11

Abstract

Abstract:

Aiming at the problems of low accuracy and automation in the process of image interpretation of large-scale instantaneous coastline extraction by synthetic aperture radar (SAR), a instantaneous coastline automatic extraction algorithm based on deep learning network is proposed. Firstly, the enhancing Lee filter is used to suppressing speckle noise for SAR image. Secondly, the features of seawater targets are extracted in four levels by upgrading the residueal network as the backbone network. Then, the four levels of features pass through global convolutional network, dense connection network and decoder network to extract the essential features. The sea water segmentation results are obtained by the upsampling process. Finally, the results can be clearly viewed by separating the coastline using Sobel operator and fusing it with the original SAR image. Compared with the coastline extraction experimental results of full convolutional network and refinement network, the experimental results show that the proposed algorithm has less false alarm and miss alarm results, and the accuracy extraction of the obtained coastline results, which has better performance.

Key words: coastline extraction, synthetic aperture radar (SAR) image shadow water extraction, deep learning, coding and decoding network

CLC Number:

TP753

Bin WANG, Guoyu WANG. Instantaneous coastline automatic extraction algorithm for SAR images based on improved deep learning network[J]. Systems Engineering and Electronics, 2021, 43(8): 2108-2115.

Figures/Tables 13

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实验	方法	PA	IOU	测试时间/s
1	FCN	0.91	0.87	2.7
	RefineNet	0.92	0.88	4.1
	本文方法	0.95	0.94	2.9
2	FCN	0.92	0.89	2.3
	RefineNet	0.92	0.90	3.5
	本文方法	0.94	0.93	2.6

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Instantaneous coastline automatic extraction algorithm for SAR images based on improved deep learning network

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