多分辨率协同遥感地块利用分类方法研究

doi:10.3724/SP.J.1047.2016.00649

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 649-654.doi: 10.3724/SP.J.1047.2016.00649

• 遥感大数据协同计算方法 • 上一篇下一篇

多分辨率协同遥感地块利用分类方法研究

夏列钢(), 王卫红, 杨海平

浙江工业大学计算机科学与技术学院,杭州 310023

收稿日期:2015-12-15 修回日期:2016-03-14 出版日期:2016-05-10 发布日期:2016-05-10
作者简介:
作者简介：夏列钢（1986-）,男,浙江绍兴人,博士,研究方向为遥感影像智能理解与信息自动提取。E-mail:xialg@zjut.edu.cn
基金资助:
国家自然科学基金项目（41271367、41371347）;国家高分辨率对地观测系统重大专项（03-Y30B06-9001-13/15-01）;广西科学研究与技术开发计划项目(14125008-1-6)

Remotely Sensed Land Patch Classification by Collaborating with Multi-Resolution Data

XIA Liegang^*(), WANG Weihong, YANG Haiping

College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Received:2015-12-15 Revised:2016-03-14 Online:2016-05-10 Published:2016-05-10
Contact: XIA Liegang E-mail:xialg@zjut.edu.cn

摘要/Abstract

摘要：

遥感影像的获取受卫星、传感器设计、大气条件等限制,往往难以兼顾时间和空间分辨率,导致由单一来源数据提取遥感信息难度较大,难以满足各种应用对信息时空分辨率越来越的需求。由此出发考虑多源数据的不同优势及其随着周期运行不断积累的多时相数据,设计了基于地块协同多种分辨率甚至多源数据的分类方法。以高空间分辨率影像为地理基准构建稳定地块分布图,这些地块在一定时间内边界与基本属性相对稳定,由此可以协同利用高时相分辨率数据反映地块在不同时间点的光谱表现,分别计算形成地块的时相变化特征,根据地类各自特点选择不同方法与数据特征完成解译,总体上以地块级监督分类完成具体类别解译。在2014年夏季青海玛多的米级土地利用分类实验中,整个植被生长季的中分数据以及冬季无云高分数据被收集用于协同分类,在解决多数据匹配、合成的基础上充分利用各数据的优势,对建设用地、水体、植被等关键类别区别对待,整体上取得了较高的解译精度,不但有效克服传统视角下数据源不足、信息缺失等问题完成了全县解译,而且保证了土地信息的时空分辨率,为生态调查与保护提供了最新最全数据支持。

关键词: 遥感地块, 多分辨率, 协同分类, 土地利用

Abstract:

The obtaining of remotely sensed imagery may affected by design of satellite, sensors, and atmospheric conditions.Normally it is difficult to balance the temporal and spatial resolution. Which leads to the hard of information extraction from a single source remote sensing data.Obviously this may impossible to meet the application demand which ask for higher and higher resolution information on spatial and temporal. Considering the different advantages of multi-source data and the accumulation of the multi-temporal data by time,we design the classification method based on the patches for multi-source data. The patches are basic geographical units which are relatively stable on boundary and properties. With these patches, other data may reflect the spectrum performance at different time or different point of view. After calculating these features we can interpret the patches with adapt methods based on the characteristics of each land class.In the land use classification experiment of Maduo in summer of 2014, many data are collecting for cooperating classification. Long term middle resolution data cover the whole vegetation growing season and cloudless high resolution data in winter are used after solving the problem of geo-matching and multi-source compositing. Because of different advantages of these data, categories like built up, water, vegetation are interpreted separately. At last we get a high total accuracy. Not only effectively overcome the traditional perspective of insufficient data source, lack of information and complete the interpretation of the county.But also ensure the spatial and temporal resolution of land information.

Key words: remote sensing patch, multi-resolution, cooperated classification, land-use

夏列钢, 王卫红, 杨海平. 多分辨率协同遥感地块利用分类方法研究[J]. 地球信息科学学报, 2016, 18(5): 649-654.DOI:10.3724/SP.J.1047.2016.00649

XIA Liegang,WANG Weihong,YANG Haiping. Remotely Sensed Land Patch Classification by Collaborating with Multi-Resolution Data[J]. Journal of Geo-information Science, 2016, 18(5): 649-654.DOI:10.3724/SP.J.1047.2016.00649

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参考文献 18

[1]	Homer C C, Huang L, Yang B W, et al.Development of a 2001 national land-cover database for the United States[J]. Photogrammetric Engineering and Remote Sensing, 2004,70(7):829-840. doi: 10.1117/1.1758729
[2]	Friedl M A, Sulla-Menashe D, Tan B, et al.MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets[J]. Remote Sensing of Environment, 2010,114(1):168-182. doi: 10.1016/j.rse.2009.08.016
[3]	Dardel C, Kergoat L, Hiernaux P, et al.Re-greening Sahel: 30years of remote sensing data and field observations (Mali, Niger)[J]. Remote Sensing of Environment, 2014,140:350-364.
[4]	Hansen M C, Potapov P V, Moore R, et al.High-resolution global maps of 21st-century forest cover change[J]. Science, 2013,342(6160):850-853. doi: 10.1126/science.1244693 pmid: 24233722
[5]	Gong P, Wang J, Yu L, et al.Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data[J]. International Journal of Remote Sensing, 2013,34(7):2607-2654.
[6]	Quartulli M, Olaizola I G.A review of EO image information mining[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013,75:11-28. doi: 10.1016/j.isprsjprs.2012.09.010
[7]	Galleguillos C, Belongie S.Context based object categorization: a critical survey[J]. Computer Vision and Image Understanding, 2010,114(6):712-722. doi: 10.1016/j.cviu.2010.02.004
[8]	Fauvel M, Chanussot J, Benediktsson J A.A spatial-spectral kernel-based approach for the classification of remote-sensing images[J]. Pattern Recognition, 2012,45(1):381-392. doi: 10.1016/j.patcog.2011.03.035
[9]	Wulder M A, White J C, Goward S N, et al.Landsat continuity: issues and opportunities for land cover monitoring[J]. Remote Sensing of Environment, 2008,112(3):955-969. doi: 10.1016/j.rse.2007.07.004
[10]	Marsetic A, Ostir K, Fras M K.Automatic orthorectification of high-resolution optical satellite images using vector roads[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(11):6035-6047. doi: 10.1109/TGRS.2015.2431434
[11]	Tulbure M G, Broich M.Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013,79:44-52. doi: 10.1016/j.isprsjprs.2013.01.010
[12]	Wozniak M, Grana M, Corchado E.A survey of multiple classifier systems as hybrid systems[J]. Information Fusion,2014,16:3-17. doi: 10.1016/j.inffus.2013.04.006
[13]	黄振国,杨君.高分一号卫星影像监测水稻种植面积研究综述[J].湖南农业科学,2014(13):76-78. doi: 10.3969/j.issn.1006-060X.2014.13.025
	[ Huang Z G, Yang J.Review of High-1 satellite image monitoring rice planting area[J]. Hunan Agricultural Sciences, 2014,13:76-78. ] doi: 10.3969/j.issn.1006-060X.2014.13.025
[14]	Blaschke T, Hay G J, Weng Q, et al.Collective sensing: integrating geospatial technologies to understand urban systems-an overview[J]. Remote Sensing, 2011,3(8):1743-1776. doi: 10.3390/rs3081743
[15]	Demir B, Bovolo F, Bruzzone L.Updating land-cover maps by classification of image time series: A novel change-detection-driven transfer learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing,2013,51(1):300-312. doi: 10.1109/TGRS.2012.2195727
[16]	张帅,邵全琴,刘纪远,等.黄河源区玛多县土地利用/覆被及景观格局变化的遥感分析[J].地球信息科学,2007,9(4):109-115,128,封2S. doi: 10.3969/j.issn.1560-8999.2007.04.020
	[ Zhang S, Shao Q Q, Liu J Y, et al. Land use and landscape pattern change in Madoi county, the source region of Yellow River[J]. Geo-Information Science, 2007, 9(4):109-115,128,Cover 2.] doi: 10.3969/j.issn.1560-8999.2007.04.020
[17]	刘纪远,刘明亮,庄大方,等.中国近期土地利用变化的空间格局分析[J].中国科学D辑,2002,32(12):1031-1040. doi: 10.3321/j.issn:1006-9267.2002.12.008
	[ Liu J Y, Liu M L, Zhuang D F, et al.Spatial pattern analysis of land-use change in recent China[J]. Science in China (Series D), 2002,32(12):1031-1040. ] doi: 10.3321/j.issn:1006-9267.2002.12.008
[18]	Wickham J D, Stehman S V, Gass L, et al.Accuracy assessment of NLCD 2006 land cover and impervious surface[J]. Remote Sensing of Environment, 2013,130:294-304. doi: 10.1016/j.rse.2012.12.001

数据类型	分辨率/m	时相	数量/景	数据量/GB
ZY3融合	2	2013、2014冬季	37	190
GF1融合	2	2014年6-9月	21	81.9
GF1-WFV	16	2014年6-9月	26	60.8
Landsat8	30	2014年6-9月	9	10.5

类别	人工用地	草地	河流	湖泊	沙地	沼泽地	裸岩石砾地	生产精度/(%)
人工用地	39	0	0	0	0	0	0	100.0
草地	1	169	2	0	1	2	1	96.0
河流	0	3	56	1	0	1	0	91.8
湖泊	0	1	1	68	0	1	0	95.8
沙地	0	1	0	0	17	0	0	94.4
沼泽地	0	6	1	1	0	16	0	66.7
裸岩石砾地	0	0	0	0	2	0	9	81.8
用户精度/(%)	97.5	93.9	93.3	97.1	85.0	80.0	90.0
总体精度/(%)									93.5
kappa系数									0.911

多分辨率协同遥感地块利用分类方法研究

Remotely Sensed Land Patch Classification by Collaborating with Multi-Resolution Data

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