Research on automatic extraction method for coastal aquaculture area using Landsat8 data

doi:10.6046/gtzyyg.2018.03.14

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(7763 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

During coastal resource monitoring, it is an effective way to extract aquaculture region using remote sensing data, whereas the water color in coastal region is complexly influenced by the distribution difference of chlorophyll-a and total suspended sediment concentration. And it would be difficult to accurately extract the aquaculture region with complex background using traditional methods. In view of the above problem, the authors proposed an algorithm for automatic coastal aquaculture area extraction combined with spectral and spatial information of aquaculture. Firstly, orthogonal subspace projection-weighted constrained energy minization method (OWCEM) was used to enhance the information of coastal aquaculture area. Secondly, by using the spatial texture information of the coastal aquaculture area, standard deviation adaptive segmentation (SDAS) method was used to automatically extract the cultivation area. In order to verify the accuracy of the proposed algorithm, the authors selected Sanggou Bay in Shandong and Sanduao Bay in Fujian as test regions and conducted the area extraction using Landsat8 data. The experimental results show that the proposed method can rapidly and accurately identify the distribution of coastal aquaculture area in complex background color and can reach about 93% accuracy rate with a low missing rate. The method could provide a new and effective means for automatic extraction of offshore aquaculture area.

Keywords coastal aquaculture region extraction standard deviation adaptive segmentation(SDAS) complex background orthogonal subspace projection-weighted constrained energy minization(OWCEM)

TP751.1

Corresponding Authors: Fu CHEN E-mail: chenfu@radi.ac.cn

Issue Date: 10 September 2018

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Yitian WU
	Fu CHEN
	Yong MA
	Jianbo LIU
	Xinpeng LI

Cite this article:

Yitian WU,Fu CHEN,Yong MA, et al. Research on automatic extraction method for coastal aquaculture area using Landsat8 data[J]. Remote Sensing for Land & Resources, 2018, 30(3): 96-105.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.14 OR https://www.gtzyyg.com/EN/Y2018/V30/I3/96

Fig.1 Sketch map for coverage of experimental areas

Fig.2 Different cultivation models of aquaculture areas

Fig.3 Flow char of extraction method(OWCEM-SDAS) for aquaculture areas

特征信息	特征指数			数学表达式
光谱特征	MNDWI			$MNDWI = (ban d 3 - ban d 6) / (ban d 3 + ban d 6)$
	AWEInsh(归一化)			$AWEInsh = 4 (ban d 3 - ban d 6) - (0.25 ban d 5 + 2.75 ban d 7) ban d 3 + ban d 5 + ban d 6 + ban d 7$
	AWEIsh(归一化)			$AWEIsh = ban d 2 + 2.5 ban d 3 - 1.5 (ban d 5 + ban d 6) - 0.25 ban d 7 ban d 2 + ban d 3 + ban d 5 + ban d 6 + ban d 7$
	CHL			$CHL = 8.48 ban d 4 / ban d 1$
	TSM			$TSM = 0.028 ban d 2 + 0.019 ban d 3 - 5.31 ban d 3 / ban d 2 + 0.537$
特征信息		特征指数	数学表达式
相关性特征		SAD	$SAD = co s - 1 (x T y ‖ x ‖ ‖ y ‖)$
		corr	$corr = x - x - T y - y - ‖ x - x - ‖ ‖ y - y - ‖$
		s	$s = ‖ x - y ‖$
		SID	$SID = D (x ‖ y) + D (y ‖ x)$		$D x ‖ y = ∑ l = 1 L p l ln p l / q l D y ‖ x = ∑ l = 1 L q l ln q l / p l$ $p l = x l / ∑ j = 1 L x j q l = y l / ∑ j = 1 L y j$

Tab.1 Feature indexes included in band expansion

Fig.4 Flow chart of OWCEM algorithm

Fig.5 Aquacultutre area extraction result in Sanggou Bay, Shandong

Fig.6 Aquacultutre area extraction result in Sanduao Bay, Fujian

Fig.7 Vertification samples of test regions distributed in reference images

Tab.2 Confusion matrix of aquaculture area extraction result in Sanggou Bay, Shandong

Tab.3 Confusion matrix of aquaculture area extraction result in Sanduao Bay, Fujian

Tab.4 comparison of extraction accuracy using obeject-oritented method and proposed method(%)

[1]	FAO. The State of World Fisheries and Aquaculture 2014[R]. Rome:FAO, 2014: 40-41.
[2]	程田飞, 周为峰, 樊伟 . 水产养殖区域的遥感识别方法进展[J]. 国土资源遥感, 2012,24(3):1-5.doi: 10.6046/gtzyyg.2012.03.01. doi: 10.6046/gtzyyg.2012.03.01
[2]	Cheng T F, Zhou W F, Fan W . Progress in the methods for extracting aquaculture areas from remote sensing data[J]. Remote Sensing for Land and Resources, 2012,24(3):1-5.doi: 10.6046/gtzyyg.2012.03.01.
[3]	马艳娟, 赵冬玲, 王瑞梅 , 等. 基于ASTER数据的近海水产养殖区提取方法[J]. 农业工程学报, 2010,26(14):120-124.
[3]	Ma Y J, Zhao D L, Wang R M , et al. Offshore aquatic farming areas extraction method based on ASTER data[J]. Transactions of the CSAE, 2010,26(14):120-124.
[4]	周小成, 汪小钦, 向天梁 , 等. 基于ASTER影像的近海水产养殖信息自动提取方法[J]. 湿地科学, 2006,4(1):64-68. doi: 10.3969/j.issn.1672-5948.2006.01.010 url: http://d.wanfangdata.com.cn/Periodical/shidkx200601010
[4]	Zhou X C, Wang X Q, Xiang T L , et al. Method of automatic extracting seaside aquaculture land based on ASTER remote sensing image[J]. Wetland Science, 2006,4(1):64-68.
[5]	林桂兰, 孙飒梅, 曾良杰 , 等. 高分辨率遥感技术在厦门海湾生态环境调查中的应用[J]. 台湾海峡, 2003,22(2):242-247. doi: 10.3969/j.issn.1000-8160.2003.02.018 url: http://d.wanfangdata.com.cn/Periodical_twhx200302018.aspx
[5]	Lin G L, Sun S M, Zeng L J , et al. Application of high resolution satellite remote sensing in survey of ecological environment at Xiamen Bay[J]. Journal of Oceanography in Taiwan Strait, 2003,22(2):242-247.
[6]	孙晓宇, 苏奋振, 周成虎 , 等. 基于RS与GIS的珠江口养殖用地时空变化分析[J]. 资源科学, 2010,32(1):71-77. url: http://d.wanfangdata.com.cn/Periodical/zykx201001010
[6]	Sun X Y, Su F Z, Zhou C H , et al. Analyses on spatial-temporal changes in aquaculture land in coastal areas of the Pearl River Estuarine[J]. Resources Science, 2010,32(1):71-77.
[7]	卢业伟, 李强子, 杜鑫 , 等. 基于高分辨率影像的近海养殖区的一种自动提取方法[J]. 遥感技术与应用, 2015,30(3):486-494. doi: 10.11873/j.issn.1004-0323.2015.3.0486
[7]	Lu Y W, Li Q Z, Du X , et al. A method of coastal aquaculture area automatic extraction with high spatial resolution images[J]. Remote Sensing Technology and Application, 2015,30(3):486-494.
[8]	Ji L Y, Geng X R, Sun K , et al. Target detection method for water mapping using Landsat 8 OLI/TIRS imagery[J]. Water, 2015,7(2):794-817. doi: 10.3390/w7020794 url: http://www.mdpi.com/2073-4441/7/2/794
[9]	Chen J, Chen J, Liao A P , et al. Global land cover mapping at 30 m resolution:A POK-based operational approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,103:7-27. doi: 10.1016/j.isprsjprs.2014.09.002 url: http://linkinghub.elsevier.com/retrieve/pii/S0924271614002275
[10]	Farrand W H, Harsanyi J C . Mapping the distribution of mine tailings in the Coeur d’Alene River Valley,Idaho,through the use of a constrained energy minimization technique[J]. Remote Sensing of Environment, 1997,59(1):64-76. doi: 10.1016/S0034-4257(96)00080-6 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425796000806
[11]	Geng X R, Ji L Y, Sun K , et al. CEM:More bands,better performance[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(11):1876-1880. doi: 10.1109/LGRS.2014.2312319 url: http://ieeexplore.ieee.org/document/6786322/
[12]	Xu H Q . Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006,27(14):3025-3033. doi: 10.1080/01431160600589179 url: http://www.tandfonline.com/doi/abs/10.1080/01431160600589179
[13]	Feyisa G L, Meilby H, Fensholt R , et al. Automated water extraction index:A new technique for surface water mapping using Landsat imagery[J]. Remote Sensing of Environment, 2014,140:23-35. doi: 10.1016/j.rse.2013.08.029 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425713002873
[14]	邬明权, 韩松, 赵永清 , 等. 应用Landsat TM影像估算渤海叶绿素a和总悬浮物浓度[J]. 遥感信息, 2012,27(4):91-95. doi: 10.3969/j.issn.1000-3177.2012.04.016 url: http://d.wanfangdata.com.cn/Periodical/ygxx201204016
[14]	Wu M Q, Han S, Zhao Y Q , et al. Quantitative estimation of chlorophyll-a and total suspended matter concentration with Landsat TM[J]. Remote Sensing Information, 2012,27(4):91-95.
[15]	Chang C I . An information-theoretic approach to spectral variability,similarity,and discrimination for hyperspectral image analysis[J]. IEEE Transactions on Information Theory, 2000,46(5):1927-1932. doi: 10.1109/18.857802 url: http://ieeexplore.ieee.org/document/857802/
[16]	Otsu N . A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems,Man,and Cybernetics, 1979,9(1):62-66. doi: 10.1109/TSMC.1979.4310076 url: http://ieeexplore.ieee.org/document/4310076/

[1]	QU Haicheng, WAND Yaxuan, SHEN Lei. Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention[J]. Remote Sensing for Natural Resources, 2022, 34(1): 43-52.
[2]	LIU Wanjun, GAO Jiankang, QU Haicheng, JIANG Wentao. Ship detection based on multi-scale feature enhancement of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 97-106.
[3]	QIU Yifan, CHAI Dengfeng. A deep learning method for Landsat image cloud detection without manually labeled data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 102-107.
[4]	WEI Hongyu, ZHAO Yindi, DONG Jihong. Cooling tower detection based on the improved RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 68-73.
[5]	LIU Shangwang, CUI Zhiyong, LI Daoyi. Multi-task learning for building object semantic segmentation of remote sensing image based on Unet network[J]. Remote Sensing for Land & Resources, 2020, 32(4): 74-83.
[6]	Kaixuan LIANG, Guifang ZHANG, Haoran ZHANG. A method for extracting alluvial fan based on DEM and remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 138-145.
[7]	Renbo SONG, Yuxin ZHU, Shangshan DING, Qiaoning HE, Xiyuan WANG, Yuexiang WANG. An automatic method for extracting skeleton lines from arbitrary polygons based on GIS spatial analysis[J]. Remote Sensing for Land & Resources, 2020, 32(1): 51-59.
[8]	Liuqing WU, Xiangyun HU. Automatic building detection of high-resolution remote sensing images based on multi-scale and multi-feature[J]. Remote Sensing for Land & Resources, 2019, 31(1): 71-78.
[9]	Yongmei ZHANG, Haiyan SUN, Yulong XU. An improved multispectral image segmentation method based on super-pixels[J]. Remote Sensing for Land & Resources, 2019, 31(1): 58-64.
[10]	Jian YU, Yunjun YAO, Shaohua ZHAO, Kun JIA, Xiaotong ZHANG, Xiang ZHAO, Liang SUN. Estimating latent heat flux over farmland from Landsat images using the improved METRIC model[J]. Remote Sensing for Land & Resources, 2018, 30(3): 83-88.
[11]	Jun YANG, Jianjie PEI. An improved ICM algorithm for remote sensing image segmentation[J]. Remote Sensing for Land & Resources, 2018, 30(3): 18-25.
[12]	Lijuan WANG, Xiao JIN, Hujun JIA, Yao TANG, Guochao MA. Change detection for mine environment based on domestic high resolution satellite images[J]. Remote Sensing for Land & Resources, 2018, 30(3): 151-158.
[13]	Jian LIAO, Xingfa GU, Yulin ZHAN, Yazhou ZHANG, Xinyu REN, Shuaiyi SHI. A method based on harmonic model for generating synthetic GF-1 images[J]. Remote Sensing for Land & Resources, 2018, 30(3): 106-112.
[14]	Liang YANG, Yi JIA, Wanshou JIANG, Guo ZHANG. High precision geometric rectification of HJ-1A/B CCD imagery based on satellite observation angle information[J]. Remote Sensing for Land & Resources, 2018, 30(2): 60-66.
[15]	Xinran ZHU, Bo WU, Qiang ZHANG. An improved CVAPS method for automatic updating of LUCC classification[J]. Remote Sensing for Land & Resources, 2018, 30(2): 29-37.

Viewed

Full text

Abstract

Cited

Shared

Discussed