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遥感技术与应用  2016, Vol. 31 Issue (3): 488-496    DOI: 10.11873/j.issn.1004-0323.2016.3.0488
数据与图像处理     
基于蚁群优化算法的遥感器波段设置探究
张路,刘良云,高建威,焦全军,贾建华
(1.西安科技大学测绘科学与技术学院,陕西西安 710054;
2.中国科学院遥感与数字地球研究所,北京 100094)
The Explore of Band Set based on Ant Colony Optimization Algorithm for Remote Sensor
Zhang Lu1,Liu Liangyun2,Gao Jianwei2,Jiao Quanjun2,Jia Jianhua1
(1.College of Geometrics,Xi’an University of science and Technology ,Xi’an 710054,China;
2.Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100094,China)
 全文: PDF(1337 KB)  
摘要:

卫星载荷研制发射后其光谱和空间观测模式固定,无法根据复杂地表的多样化需求进行实时灵活调整,且目前遥感器波段设置尚不完善还存在优化空间.引进基于蚁群优化算法的波段选择方法(AntColonyOptimization basedBandSelection,ACOBS),结合北美区域33景AVIRIS航空高光谱图像,开展了不同区域、不同地表覆盖类型的高光谱波段优选研究,发现各地表类型优选波段组合存在一定差异,其中4波段组合中红光、近红外波段为2个共同入选波段,6波段组合中绿光、红光、短波红外波段为3个共有波段,8波段组合中紫光、绿光、红光、红边、近红外1、近红外2、短波红外1、短波红外2为8个共有入选波段,其他入选波段与地表覆盖类型有关.在此基础上,进一步开展了多光谱卫星波段设置评价研究,发现:4波段优化方案中,绿光、红光、近红外波段1 (770~895nm)、近红外波段2(900~1350nm)为最优波段组合;6波段优化方案中,绿、红、红边、近红外1(770~895nm)、近红外2(900~1350nm)、短波红外1(1560~1660nm)为最优波段组合;8波段优化方案中,蓝、绿、红、红边、近红外1(770~895nm)、近红外2(900~1350nm)、短波红外1(1560~1660nm)和短波红外2(2100~2300nm)为最优波段组合.研究结果表明Land satTM OLI、SPOT等陆地资源遥感器波段设置还存在一定优化调整空间,特别是红边波段在目前传感器波段设置中没有得到足够重视.

关键词: 波段设置蚁群优化算法波段选择高光谱数据    
Abstract:

Currently,both spectral parameters settings and spatial observation mode of on\|orbit satellite sensors are fixed and cannot be flexibly adjusted in real\|time according to the diversified needs of observing complex ground surface.However,the spectral band setting of current remote sensing sensor is still needed to be optimized.In this paper,using the band selection method based on the ant colony optimization algorithm (Ant Colony Optimization\|based Band Selection,ACOBS),Combining 33 views AVIRIS Airborne Hyperspectral image in North America,carried out optimum bands combination research of hyperspectral in different regions,different land cover types.The result shows that optimum combinations of spectral bands are different for different land cover types.Red and near infrared bands are the candidates in optimal 4\|band combination,green、red and short\|wave infrared bands are the candidates in optimal 6\|band combination,purple,green,red,red edge,near infrared1,near infrared2,SWIR1and SWIR 2 are the candidates in optimal 8\|band combination,and other selected bands in different band combinations are different depend on different land cover types.Optimum spectral bands for multi\|spectral satellite sensor was further analyzed,the result shows that optimal 4\|band combination is green,red,near\|infrared 1 (770~895 nm) and near\|infrared 2 (900~1 350 nm); the optimal 6\|band combination is green,red,red edge,near infrared 1(770~895 nm) and near infrared 2(900~1 350 nm),SWIR1(1 560~1 660 nm),the optimal 8\|band combination is blue,green,red,red edge,near\|infrared 1(770~895 nm),near\|infrared 2(900~1 350 nm),SWIR1(1 560~1 660 nm) and SWIR 2 (2 100~2 300 nm).It is indicated that spectral bands setting of Landsat TM/OLI,SPOT is not optimum for observing earth surface,and red edge band is neglected in current optical multi\|spectral satellite sensor.

Key words: Sensor    Band setting    Ant colony optimization    Band selection    Hyperspectral data
收稿日期: 2015-04-28 出版日期: 2016-07-19
:  TP701  
基金资助:

国家自然科学基金项目“中国科学院新型对地观测系统科技创新交叉合作团队”(41325004),国家科技支撑计划课题(2013BAC03B02).

通讯作者: 刘良云(1975-),男,湖南邵阳人,研究员,主要从事植被定量遥感研究.Email:liuly@radi.ac.cn.   
作者简介: 张路(1989-),女,河北沧州人,硕士研究生,主要从事高光谱遥感数据处理研究.Email:zhanglumengcun@163.com.
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引用本文:

张路,刘良云,高建威,焦全军,贾建华. 基于蚁群优化算法的遥感器波段设置探究[J]. 遥感技术与应用, 2016, 31(3): 488-496.

Zhang Lu,Liu Liangyun,Gao Jianwei,Jiao Quanjun,Jia Jianhua. The Explore of Band Set based on Ant Colony Optimization Algorithm for Remote Sensor. Remote Sensing Technology and Application, 2016, 31(3): 488-496.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.3.0488        http://www.rsta.ac.cn/CN/Y2016/V31/I3/488

[1]Jia Yonghong,Li Deren,Liu Jilin.Com-parison of IHS Transofrmation of Integrating SAR and TM Images[J].Journal of Remote Sensing,1998,2(2):103-106.[贾永红,李德仁,刘继林.四种IHS变换用于SAR与TM影像复合的比较[J].遥感学报,1998,2(2):103-106.]

[2]Chavez P S,Berlin G L,Sowers L B.Statistical Method for Selecting Landsat MSS Ratios[J].Journal of Applied Photographic Engineering,1982,8(1):23-30.

[3]Sheffield C.Selecting Band Combinations from Multis-pectral Data[J].Photogrammetric Engineering and Remote Sensing,1985,51:681-687.

[4]Su Hongjun,Du Peijun,Sheng Yehua.Study on Band Selection Algorithms of Hyperspectral Image Data[J].Application Research of Computers,2008,(4):1093-1096.[苏红军,杜培军,盛业华.高光谱影像波段选择算法研究[J].计算机应用研究,2008,(4):1093-1096].

[5]Yang Jinhong,Yin Qiu,Zhou Ning.An Improved Method of Hyperspectral Remote Sensing Data Adaptive Band Selection[J].Remote Sensing Technology and Application,2007,(4):513-519.[杨金红,尹球,周宁.一种改进的高光谱数据自适应波段选择方法[J].遥感技术与应用,2007,(4):513-519].

[6]Liu Xuesong,Ge Liang,Wang Bin,et al.An Unsupervised Band Selection Algorithm for Hyperspectral Imagery based on Maximal Information[J].Journal of Infrared and Millimeter Waves,2012,(2):166-170,176.[刘雪松,葛亮,王斌,等.基于最大信息量的高光谱遥感图像无监督波段选择方法[J].红外与毫米波学报,2012,(2):166-170,176].

[7]Wang Liguo,Gu Yanfeng,Zhang Ye.Band Selection Method based on Combination of Support Vector Machines and Subspatial Partition[J].Systems Engineering and Electronics,2005,(6):974-977.[王立国,谷延锋,张晔.基于支持向量机和子空间划分的波段选择方法[J].系统工程与电子技术,2005,(6):974-977].

[8]Colorni A,Dorigo M,Maniezzo V.Distributed Optimization by Ant Colonies[C]//Proceedings of the First European Conference on Artificial Life Paris,Frarce,1991,142:134-142.

[9]Zhang B,Sun X,Gao L R,et al.Endmember Extraction of Hyperspectral Remote Sensing Images based on the Ant Colony Optimization(ACO) Algorithm[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(7):2635.

[10]Gholizadeh H,Mojaradi B,Valadan Zoej M J.Local Prototype Space-based Band Selection for Hyperspectral Subpixel Analysis[J].PFG Photogrammetrie,Fernerkundung,Geoinformation,2015:20155.

[11]Zhou S,Zhang J,Su B K.Feature Selection and Classification based on Ant Colony Algorithm for Hyperspectral Remote Sensing Images[C]//CISP'09.2nd International Congress on Image and Signal Processing.IEEE,2009:1-4.

[12]Chien S,Cichy B,Davies A,et al.An Autonomous Earth Observing Sensorweb[C]//IEEE International Conference on Sensor Networks,Ubiquitous,and Trustworthy Computing,2006,1:8.

[13]Barbara V M,Ruddick K.The Compact High Resolution Imaging Spectrometer (CHRIS):The Future of Hyperspectral Satellite Sensors[C]//Imagery of Oostende Coastal and Inland Waters,Proceedings of the Airborne Imaging Spectroscopy workshop,Brugge,2004.

[14]Zhou G,Baysal O,Kaye J,Habib S,et al.Concept Design of Future Intelligent Earth Observing Satellites[J].International Journal of Remote Sensing,2004,2667-2685.

[15]Li Deren,Shen Xin.OnIntelligent Earth Observation Systems[J].Science of Surveying and Mapping,2005,(4):9-11.[李德仁,沈欣.论智能化对地观测系统[J].测绘科学,2005,4:9-11.

[16]Zhang Bing.Intelligent Remote Sensing Satellite System[J].Journal of Remote Sensing,2011,15(3):415-431.[张兵.智能遥感卫星系统[J].遥感学报,2011,15(3):415-431.]

[17]Du Q.Band Selection and Its Impact on Target Detection and Classification in Hyperspectral Image Analysis[C]//2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data,2003:374-377.

[18]Jia S,Ji Z,Qian Y,Shen L.Unsupervised Band Selection for Hyperspectral Imagery Classification without Manual band Removal[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2012,5(2):531-543.〖JP〗

[19]Liu X S,Ge L,Wang B,et al.An Unsupervised band Selection Algorithm for Hyperspectral Imagery based on Maximal Information[J].Journal of Infrared and Millimeter Waves,2012,31(2):166-170.

[20]Yang H,Du Q,Chen G.Unsupervised Hyperspectral band Selection Using Graphics Processing Units[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2011,4(3):660-668.

[21]Stellacci A M,Castrignanò A,Troccoli A,et al.Selecting Optimal Hyperspectral Bands to Discriminate Nitrogen Status in Durum Wheat:a Comparison of Statistical Approaches.[J].Environmental Monitoring and Assessment,2016,1883.

[22]Huang B C,Nian Y J,Wan J W.Distributed Lossless Compression Algorithm for Hyperspectral Images based on Classification[J].Spectroscopy Letters,2015,487.

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