遥感土地覆被分类的空间尺度响应研究

doi:10.12082/dqxxkx.2018.170360

地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (2): 246-253.doi: 10.12082/dqxxkx.2018.170360

遥感土地覆被分类的空间尺度响应研究

徐凯健^1,²(), 田庆久^1,^*(), 杨闫君^1,², 徐念旭^1,²

1. 南京大学国际地球系统科学研究所,南京 210023
2. 江苏省地理信息技术重点实验室,南京 210023

收稿日期:2017-08-03 修回日期:2017-10-10 出版日期:2018-03-02 发布日期:2018-03-02
通讯作者: 田庆久 E-mail:phoenix-max@qq.com;tianqj@nju.edu.cn
作者简介:
作者简介：徐凯健(1991-),男,博士生,研究方向为多尺度环境遥感应用。E-mail: phoenix-max@qq.com
基金资助:
国家重点研发计划重点专项（2017YFD0600903）;国家科技重大专项（03-Y20A04-9001-17/18、30-Y20A29- 9003-15/17）

Response of Spatial Scale for Land Cover Classification of Remote Sensing

XU Kaijian^1,²(), TIAN Qingjiu^1,^*(), YANG Yanjun^1,², XU Nianxu^1,²

1. International Institute for Earth System Science, Nanjing University, Nanjing 210023, China
2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

Received:2017-08-03 Revised:2017-10-10 Online:2018-03-02 Published:2018-03-02
Contact: TIAN Qingjiu E-mail:phoenix-max@qq.com;tianqj@nju.edu.cn
Supported by:
National Key R&D Program of China, No.2017YFD0600903;High-resolution Earth Observation System Project of China, No.03-Y20A04-9001-17/18, 30-Y20A29-9003-15/17

摘要/Abstract

摘要：

不同空间分辨率遥感影像对区域土地覆被类型识别精度的影响是目前土地资源遥感研究中的热点议题。本文基于准同步的卫星传感器影像,以福建省长汀县河田盆地为研究区,结合野外调查的实验样本,依次采用最大似然法（MLC）、支持向量机（SVM）和人工神经网络（ANN）3种分类器,分析土地覆被分类结果在中高空间尺度序列（1~50 m）下的变化响应特征。结果表明：不同空间尺度下的地物分类结果存在显著差异（P<0.05）,其中总分类精度和Kappa系数均随影像分辨率的降低而先升高后降低,并于4 m分辨率处达到峰值,该结果与各类地物光谱反射率的空间尺度变化特征密切相关;而不同分类器对各空间尺度影像分类结果的影响程度差异较大（P<0.05）,其中SVM的分类精度最优,MLC次之,ANN的结果较差。此外,伴随影像空间分辨率的降低,不同土地覆被类型面积提取结果的变化规律不同,导致同类地物在不同空间尺度下的提取结果出现较大差异,表明在使用多源分辨率遥感数据进行土地监测等相关研究时,其伴随的结果误差不容忽视。

关键词: 遥感分类, 分类器, 高分卫星, 空间分辨率, 尺度效应

Abstract:

Classification based on remote sensing data has been widely applied in land cover mapping and the dynamic change monitoring research, of which the consequence is always strongly affected by spatial resolution of the used images. However, the response of multi-resolution images to remote sensing classification is still highly uncertain. Satellite observation could supply more and more multi-resolution images covering the same area at the same time and it would provide abundant data and technical support for study of remote sensing classification. In this study, the Hetian basin of Changting County in Fujian Province, was selected as a case to examine the performance of three typical classifiers (Maximum Likelihood Classification, MLC; Support Vector Machine, SVM; Artificial Neural Network, ANN). They were applied to satellite observations of temporal quasi-synchronous and multi-spatial resolution from medium to high spatial resolution (1~50 m) and we investigated the links between spatial resolution and remote sensing classification. Then, we also analyzed the spatial scale difference of spectrum reflectance, recognition accuracy and area extraction of five major land types (including arable land, forest land, water area, bare land and construction land) of the data with seven spatial resolution levels of 1, 2, 4, 8, 16, 30, and 50 m. They were supported with GF-1 PMS (pan and multi-spectra sensor), GF-2 PMS, GF-1 WFV (wide field view), Landsat-8 OLI (operational land imager) and GF-4 PMS data. 1845 recorded points observed in field survey were taken as training samples and validation samples. The results showed that along with the change of image spatial resolution from 1 to 50 m, (1) the mean spectra of bare land and construction land remained stable and no obvious changes occurred to water body, while the mean spectra of arable land and forest land decreased significantly when image resolution coarser than 4 m. The standard deviations of water body, bare land and construction land all increased constantly, while the standard deviations of arable land and forest land almost maintained stable. (2) The overall accuracy gradually decreased from 94.97±2.5% to 79.03±2.25% across the three classifiers, showing a gradually downward trend. Meanwhile, Kappa coefficient also gradually decreased from 0.93±0.03 to 0.72±0.03, which indicated that the accuracy of land cover classification was closely and sensitively related to the resolution of remote sensing images (P<0.05). (3) The calculation errors of the land types area would become larger as the image tend to be coarser, of which the area of arable land, bare land and construction land decreased significantly, the area of forest land increased, and the change of water body was not evident. The results above confirmed that when using multi-resolution images to generate land cover area or making area comparison refer to time serial data results, the errors from spatial database of various multi-scale could not be neglected, which would be more suitable to make the multi-scale transform for spatial effect correction. Our framework demonstrated the regular pattern of multiscale remote sensing classification and provided the prerequisite for scale conversion of classification products with different resolution in the future.

Key words: classification, classifier, high-resolution satellite, spatial resolution, scale effect

徐凯健, 田庆久, 杨闫君, 徐念旭. 遥感土地覆被分类的空间尺度响应研究[J]. 地球信息科学学报, 2018, 20(2): 246-253.DOI:10.12082/dqxxkx.2018.170360

XU Kaijian,TIAN Qingjiu,YANG Yanjun,XU Nianxu. Response of Spatial Scale for Land Cover Classification of Remote Sensing[J]. Journal of Geo-information Science, 2018, 20(2): 246-253.DOI:10.12082/dqxxkx.2018.170360

图/表 6

图1

表1

表2

图2

图3

图4

参考文献 34

[1]	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, 2013,34:2607-2654. doi: 10.1080/01431161.2012.748992
[2]	赫英明,王汉杰,张洪峰.遥感数据的土地覆盖分类[J].解放军理工大学学报:自然科学版,2011,12(3):294-300.
	[ He Y M, Wang H J, Zhang H F.Land cover classification based on remote sensing data[J]. Journal of PLA University of Science and Technology: Natural Science Edition, 2011,12(3):294-300. ]
[3]	Sexton J O, Song X P, Feng M, et al.Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error[J]. International Journal of Digital Earth, 2013,6(5):427-448. doi: 10.1080/17538947.2013.786146
[4]	Zheng B J, Myint S W, Thenkabail P S, et al.A support vector machine to identify irrigated crop types using time-series Landsat NDVI data[J]. International Journal of Applied Earth Observation and Geoinformation, 2015,34:103-112. doi: 10.1016/j.jag.2014.07.002
[5]	Pu R L, Susan B.Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2017,54:145-158. doi: 10.1016/j.jag.2016.09.011
[6]	杨红卫,童小华.中高分辨率遥感影像在农业中的应用现状[J].农业工程学报,2012,28(24):138-149.
	[ Yang H W, Tong X H.Application status of remote sensing images with middle and high resolution in agriculture[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012,28(24):138-149. ]
[7]	Lu D, Weng Q.A survey of image classification methods and techniques for improving classification performance[J]. International Journal of Remote Sensing, 2007,28(5):823-870. doi: 10.1080/01431160600746456
[8]	李小文,王祎婷.定量遥感尺度效应刍议[J].地理学报,2013,68(9):5-11. doi: 10.11821/dlxb201309001
	[ Li X W, Wang Y T.Prospects on future developments of quantitative remote sensing[J]. Acta Geographica Sinica, 2013,68(9):5-11. ] doi: 10.11821/dlxb201309001
[9]	Woodcock C E, Strahler A H.The factor of scale in remote sensing[J]. Remote Sensing of Environment, 1987,21(3):311-332. doi: 10.1016/0034-4257(87)90015-0
[10]	Atkinson P M, Aplin P.Spatial variation in land cover and choice of spatial resolution for remote sensing[J]. International Journal of Remote Sensing, 2004,25(18):3687-3702. doi: 10.1080/01431160310001654383
[11]	Ballanti L, Blesius L, Hines E, et al.Tree species classification using hyperspectral imagery: A comparison of two classifiers[J]. Remote Sensing, 2016,8(6):445-462. doi: 10.3390/rs8060445
[12]	Roth K L, Roberts D A, Dennison P E, et al.The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data[J]. Remote Sensing of Environment, 2015,171:45-57. doi: 10.1016/j.rse.2015.10.004
[13]	Meddens A J H, Hicke J A, Vierling L A. Evaluating the potential of multispectral imagery to map multiple stages of tree mortality[J]. Remote Sensing of Environment, 2011,115:1632-1642. doi: 10.1016/j.rse.2011.02.018
[14]	王苗苗,周蕾,王绍强,等.空间分辨率对总初级生产力模拟结果差异的影响[J].地理研究,2016,35(4):617-626.
	[ Wang M M, Zhou L, Wang S Q, et al.An analysis of the simulation differences of gross primary productivity resulting from the spatial resolution[J]. Geographical Research, 2016,35(4):617-626. ]
[15]	Yakar M, Yilmaz H M, Yurt K.The effect of grid resolution in defining terrain surface[J]. Experimental Techniques, 2010,34(6):23-29. doi: 10.1111/j.1747-1567.2009.00553.x
[16]	赵磊. 基于多源遥感数据的区域景观格局尺度效应[J].遥感信息,2009,24(4):55-61.
	[ Zhao L.Scale effect of regional landscape patterns based on the multi-source remote sensing data[J]. Remote Sensing Information, 2009,24(4):55-61. ]
[17]	韩鹏,龚健雅,李志林,等.遥感影像分类中的空间尺度选择方法研究[J].遥感学报,2010,14(3):507-518.
	[ Han P, Gong J Y, Li Z L, et al.Selection of optimal scale in classification of remotely sensed images[J]. Journal of Remote Sensing, 2010,14(3):507-518. ]
[18]	Wang G X, Gertner G, Anderson A B.Up-scaling methods based on variability-weighting and simulation for inferring spatial information across scales[J]. International Journal of Remote Sensing, 2004,25(22):4961-4979. doi: 10.1080/01431160410001680428
[19]	韩鹏,龚健雅,李志林,等.遥感影像空间尺度上推方法的评价[J].遥感学报,2008,12(6):964-971. doi: 10.3321/j.issn:1007-4619.2008.06.020
	[ Han P, Gong J Y, Li Z L, et al.Evaluation of the effect of aggregation methods for remote sensing images[J]. Journal of Remote Sensing, 2008,12(6):964-971. ] doi: 10.3321/j.issn:1007-4619.2008.06.020
[20]	胡云锋,徐芝英,刘越,等.空间尺度上推方法的精度评价——以内蒙古锡林郭勒盟土地利用数据为例[J].地理研究,2012,31(11):1961-1972. doi: 10.11821/yj2012110004
	[ Hu Y F, Xu Z Y, Liu Y, et al.Accuracy analysis of up-scaling data: A case study with land use data in Xilin Guole of Inner Mongolia, China[J]. Geographical Research, 2012,31(11):1961-1972. ] doi: 10.11821/yj2012110004
[21]	孙攀,董玉森,陈伟涛,等.高分二号卫星影像融合及质量评价[J].国土资源遥感,2016,28(4):108-113. doi: 10.6046/gtzyyg.2016.04.17
	[ Sun P, Dong Y S, Chen W T, et al.Research on fusion of GF-2 imagery and quality evaluation[J]. Remote Sensing for Land and Resources, 2016,28(4):108-113. ] doi: 10.6046/gtzyyg.2016.04.17
[22]	Nikolakopoulos K G.Spatial Resolution enhancement of hyperion hyperspectral data[J]. IEEE Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009,8:1-4. doi: 10.1109/WHISPERS.2009.5288996
[23]	Guo J H, Yang F, Tan H, et al.Shadow extraction from high-resolution remote sensing images based on gram-schmidt orthogonalization in lab space[C]. 3rd International Symposium of Space Optical Instruments and Applications, Springer Proceedings in Physics, 2017,192:321-328.
[24]	Bernstein L S, Jin X M, Gregor B, et al.Quick atmospheric correction code: Algorithm description and recent upgrades[J]. Optical Engineering, 2012,51(11):1719. doi: 10.1117/1.OE.51.11.111719
[25]	Hicke J A, Logan J A.Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery[J]. International Journal of Remote Sensing, 2009,30:4427-4441. doi: 10.1080/01431160802566439
[26]	Bruzzone L, Persello C.A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples[J]. IEEE Transactions on Geoscience & Remote Sensing, 2009,47:2142-2154. doi: 10.1109/TGRS.2008.2011983
[27]	Shao Y, Lunetta R S.Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012,70:78-87. doi: 10.1016/j.isprsjprs.2012.04.001
[28]	Murthy C S, Raju P V,Badrinath K V S. Classification of wheat crop with multi-temporal images: Performance of maximum likelihood and artificial neural networks[J]. International Journal of Remote Sensing, 2003,23:871-890. doi: 10.1080/0143116031000070490
[29]	李颖,李耀辉,王金鑫,等. SVM和ANN在多光谱遥感影像分类中的比较研究[J].海洋测绘,2016,36(5):19-22. doi: 10.3969/j.issn.1671-3044.2016.05.005
	[ Li Y, Li Y H, Wang J X, et al.A comparative study of SVM and ANN in multispectral image classification[J]. Hydrographic Surveying and Charting, 2016,36(5):19-22. ] doi: 10.3969/j.issn.1671-3044.2016.05.005
[30]	Lucas I F, Frans J M.Accuracy assessment of satellite derived land-cover data: A review[J]. Photogrammetric Engineering & Remote Sensing, 1994,60(4):410-432. doi: 10.1016/0924-2716(94)90066-3
[31]	徐涵秋,刘智才,郭燕滨. GF-1 PMS1与ZY-3 MUX传感器NDVI数据的对比分析[J].农业工程学报,2016,32(8):148-154.
	[ Xu H Q, Liu Z C, Guo Y B.Comparison of NDVI data between GF-1 PMS1 and ZY-3 MUX sensors[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(8):148-154. ]
[32]	王利民,刘佳,高建孟,等.冬小麦面积遥感识别精度与空间分辨率的关系[J].农业工程学报,2016,32(23):152-160. doi: 10.11975/j.issn.1002-6819.2016.23.021
	[ Wang L M, Liu J, Gao J M, et al.Relationship between accuracy of remote sensing identification of winter wheat area and spatial resolution[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(23):152-160. ] doi: 10.11975/j.issn.1002-6819.2016.23.021
[33]	Li N, Xie G D, Zhou D, et al.Remote sensing classification of Marsh Wetland with different resolution images[J]. Journal of Resources and Ecology, 2016,7(2):107-114. doi: 10.5814/j.issn.1674-764x.2016.02.005
[34]	马红梅,王苗苗,刘勇.多源遥感数据土地覆被空间尺度效应探讨[J].遥感信息,2017,32(2):149-155.
	[ Ma H M, Wang M M, Liu Y.Spatial scale effects of land cover based on multi-source remote sensing data[J]. Remote Sensing Information, 2017,32(2):149-155. ]

传感器类型	波段名称	波长信息/μm	空间分辨率/m	成像日期
GF-2 PMS1	蓝/绿	0.45~0.52/0.52~0.59	1/4	2015-08-27 2015-08-27
GF-2 PMS1	红/近红外	0.63~0.69/0.77~0.89	1/4	2015-08-27 2015-08-27
GF-1 PMS2	蓝/绿	0.45~0.52/0.52~0.59	2/8	2015-09-17 2015-09-17
GF-1 PMS2	红/近红外	0.63~0.69/0.77~0.89	2/8	2015-09-17 2015-09-17
GF-1 WFV4	蓝/绿	0.45~0.52/0.52~0.59	16	2015-08-03 2015-08-03
GF-1 WFV4	红/近红外	0.63~0.69/0.77~0.89	16	2015-08-03 2015-08-03
Landsat-8 OLI	蓝/绿	0.45~0.52/0.53~0.60	30	2015-09-18 2015-09-18
Landsat-8 OLI	红/近红外	0.63~0.68/0.85~0.89	30	2015-09-18 2015-09-18
GF-4 PMS	蓝/绿	0.45~0.52/0.52~0.60	50	2016-08-01 2016-08-01
GF-4 PMS	红/近红外	0.63~0.69/0.76~0.90	50	2016-08-01 2016-08-01

传感器	波段名称	信息熵			对比度
传感器	波段名称	融合前		融合后	融合前	融合后
GF-1 PMS	蓝光	1.59	2.02		29.9	41.96
	绿光	1.62	2.02		27.07	43.43
	红光	1.65	2.02		27.23	40.51
	近红外	1.65	2.01		24.54	42.02
GF-2 PMS	蓝光	1.71	2.1		35.25	49.08
	绿光	1.81	2.09		32.07	46.65
	红光	1.73	2.09		35.05	45.56
	近红外	1.89	2.08		27.15	44.16

遥感土地覆被分类的空间尺度响应研究

Response of Spatial Scale for Land Cover Classification of Remote Sensing

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 34

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	李思进, 代文, 熊礼阳, 汤国安. DEM分辨率对黄土侵蚀沟形态特征表达的不确定性分析[J]. 地球信息科学学报, 2020, 22(3): 338-350.
[2]	孙思奥,任宇飞,张蔷. 多尺度视角下的青藏高原水资源短缺估算及空间格局[J]. 地球信息科学学报, 2019, 21(9): 1308-1317.
[3]	陈昭, 罗小波, 高阳华, 叶勤玉, 王书敏. 基于半变异函数的重庆市地表温度空间异质性建模及多尺度特征分析[J]. 地球信息科学学报, 2019, 21(7): 1051-1060.
[4]	平博, 孟云闪, 苏奋振. 面向GF-1 WFV数据和MODIS数据的时空融合算法对比分析[J]. 地球信息科学学报, 2019, 21(2): 157-167.
[5]	刘浩, 骆剑承, 黄波, 杨海平, 胡晓东, 徐楠, 夏列钢. 基于特征压缩激活Unet网络的建筑物提取[J]. 地球信息科学学报, 2019, 21(11): 1779-1789.
[6]	袁鹏飞, 黄荣刚, 胡平波, 杨必胜. 基于多光谱LiDAR数据的道路中心线提取[J]. 地球信息科学学报, 2018, 20(4): 452-461.
[7]	周文臻, 陈楠. 天文辐射空间分布与尺度效应研究[J]. 地球信息科学学报, 2018, 20(2): 186-195.
[8]	陈伟锋, 毛政元, 徐伟铭, 许锐. 基于Adaboost的高分遥感影像自动变化检测方法[J]. 地球信息科学学报, 2018, 20(12): 1756-1767.
[9]	郑慧祯, 陈燕红, 丁威, 潘文斌, 蔡芫镔. 地表温度扰动特性及其与建设用地扩张的关系[J]. 地球信息科学学报, 2018, 20(10): 1529-1540.
[10]	杨闫君, 田庆久, 占玉林, 陶波, 徐凯健. 空间分辨率与纹理特征对多光谱遥感分类的影响[J]. 地球信息科学学报, 2018, 20(1): 99-107.
[11]	陈扬洋, 明冬萍, 徐录, 赵璐. 高空间分辨率遥感影像分割定量实验评价方法综述[J]. 地球信息科学学报, 2017, 19(6): 818-830.
[12]	吴田军, 马江洪. 历史解译知识引导下组合遥感图谱特征的变化检测方法[J]. 地球信息科学学报, 2016, 18(5): 655-663.
[13]	黄骁力, 汤国安, 刘凯. DEM分辨率对地形纹理特征提取的影响[J]. 地球信息科学学报, 2015, 17(7): 822-.
[14]	冯桂香, 明冬萍. 分形定量选择遥感影像最佳空间分辨率的方法与实验[J]. 地球信息科学学报, 2015, 17(4): 478-485.
[15]	江昱, 葛咏, 陈跃红, 宋海荣, 胡建龙. 亚像元制图适应性分析与评价——以天津市津南区和北京市海淀区土地覆被制图为例[J]. 地球信息科学学报, 2015, 17(10): 1215-1223.