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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (4) : 120-129     DOI: 10.6046/gtzyyg.2020.04.17
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An improved double-branch network method for intelligently extracting marine cage culture area
ZHENG Zhiteng1(), FAN Haisheng2, WANG Jie3, WU Yanlan1,4, WANG Biao1(), HUANG Tengjie2
1. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2. Zhuhai Obit Aerospace Technology Co., Ltd., Zhuhai 519080, China
3. Geodetic Data Processing Center, Ministry of Natural Resources, Xi'an 710054, China
4. Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China
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Abstract  

For the traditional remote sensing image data extraction, the accuracy of the cage information is low, and there exist the problems of “different object with the same spectrum” and “salt and salt” noise. Based on the Gaofen-2 satellite (“GF-2”) data, this paper proposes an improved double-branch network model cage information extraction method. The model uses the dense connection block to extract the spatial feature information of the cage on the spatial coding path, obtains the global context information of the cage quickly by using the global average pooling on the global coding path, and finally enriches the detailed information of the cage space through feature fusion. And deep discriminant feature information improves the extraction accuracy of the cage. The method has achieved scores of 87.37%, 72.56%, and 82.47% on the three evaluation indicators of precision, IOU, and F1, respectively, which are 7.82, 4.12, and 4.64 percentage points higher than the traditional method with the highest accuracy, respectively. The deep learning model has also achieved an increase of 8.43 and 8.69 percentage points in IOU and F1. Experiments show that the method can meet the extraction work of sea cage culture area, and can provide technical support for the regulation and regulation of offshore sea cage culture.
Keywords cage culture      remote sensing image      double-branch network      deep learning     
:  TP79  
Corresponding Authors: WANG Biao     E-mail: zhitengvip@163.com;wangbiao-rs@ahu.edu.cn
Issue Date: 23 December 2020
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Zhiteng ZHENG
Haisheng FAN
Jie WANG
Yanlan WU
Biao WANG
Tengjie HUANG
Cite this article:   
Zhiteng ZHENG,Haisheng FAN,Jie WANG, et al. An improved double-branch network method for intelligently extracting marine cage culture area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 120-129.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.04.17     OR     https://www.gtzyyg.com/EN/Y2020/V32/I4/120
Fig.1  Flow chart of network structure
Fig.2  Spatial coding path of dense connection block combination
Fig.3  Global coding path of extended semantic module combination
省份 地区 数量
福建省 莆田市 5
泉州市 7
漳州市 11
宁德市 11
广东省 阳江市 2
潮州市 7
湛江市 6
海南省 万宁市 3
陵水黎族自治区 5
Tab.1  Sample image data information(幅)
Fig.4  Samples of cage
Fig.5  Results show
影像数据 p IOU F1
影像1 89.44 83.28 90.68
影像2 91.47 74.84 84.53
影像3 88.91 66.25 78.44
影像4 79.65 65.85 76.24
平均值 87.37 72.56 82.47
Tab.2  Test accuracy(%)
方法 p IOU F1
决策树 73.36 66.00 75.89
SVM 79.55 68.44 77.83
面向对象 67.23 61.13 71.91
本文方法 87.37 72.56 82.47
Tab.3  Comparison of extraction accuracy with traditional methods(%)
Fig.6  Comparison of extraction results with traditional methods
方法 p IOU F1
U-Net 69.79 45.75 55.54
SegNet 84.02 50.74 61.93
DCCN 88.00 64.13 73.78
本文方法 87.37 72.56 82.47
Tab.4  Comparison of extraction accuracy with classic deep learning network model(%)
Fig.7  Comparison of extraction results with classic deep learning network model
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