基于全卷积网络模型的高分遥感影像内陆网箱养殖区提取

doi:10.3976/j.issn.1002-4026.2022.02.001

Abstract

Abstract:

The extraction of cage aquacultural areas was investigated using high-resolution GF-1 and GF-2 remote sensing images from northern Fujian Province. Image enhancement was performed by correction, fusion, and cropping. The sample database of inland cage culture areas of two kinds of images was constructed; The sample bank is used to train the in-depth learning fully convolutional networks (FCN) model extracted from inland cage culture area and verify the accuracy. The results of the test experiment show that the F-measure of GF-1 and GF-2 reaches 83.37% and 92.56%,respectively. It shows that the inland cage culture area extraction based on FCN has high accuracy, and can be used for large-scale inland cage acquaculture area extraction, which provides an important basis for the monitoring of inland aquaculture area.

Key words: deep learning, FCN model, data enhancement, high-resolution remote sensing image, GF satellite, inland cage aquacultural area, aquacultural area extraction

CLC Number:

TP79

LI Lian-wei,ZHANG Yuan-yu,YUE Zeng-you,XUE Cun-jin,FU Yu-xuan,XU Yang-feng. Extracting inland cage aquacultural areas from high-resolution remote sensing images using fully convolutional networks model[J].Shandong Science, 2022, 35(2): 1-10.

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References 28

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卫星名称	成像时间	幅宽/km	传感器	波谱范围/μm	空间分辨率/m
GF-1	2018-10-30	60	全色	0.45~0.90	2
			多光谱	0.45~0.52	8
				0.52~0.59
				0.63~0.69
				0.77~0.89
GF-2	2019-03-20	45	全色	0.45~0.90	1
			多光谱	0.45~0.52	4
				0.52~0.59
				0.63~0.69
				0.77~0.89

软件环境		硬件环境
项目	内容	项目	内容
系统	Windows server 2016	CPU	InterXeno W-2125
语言	Python 3.6	内存	16 G
运算平台	CUDA10.0	GPU	Quadro P6000
加速器	CUDNN7.5	显存	24 G
学习框架	TensorFlow1.8

		真值
		正确	错误
提取	正确	TP	FP
提取	错误	FN	TN

影像		GF-1				GF-2
区域		A		B		A		B
区域		养殖区	非养殖区	养殖区	非养殖区	养殖区	非养殖区	养殖区	非养殖区
提取	养殖区	9 977	1 529	11 363	1 690	72 852	6 441	101 203	10 559
提取	非养殖区	1 970	986 524	3 417	143 530	2 965	3 917 742	9 021	3 879 217
精确率		86.71%		87.05%		91.88%		90.55%
召回率		83.51%		76.88%		96.09%		91.82%
F值		85.08%		81.65%		93.94%		91.18%
准确率		99.65%		96.81%		99.76%		99.51%
Kappa系数		0.85		0.8		0.94		0.91

Extracting inland cage aquacultural areas from high-resolution remote sensing images using fully convolutional networks model

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