土地覆盖遥感制图方法研究现状与展望

doi:10.13474/j.cnki.11-2246.2019.0342

测绘通报 ›› 2019, Vol. 0 ›› Issue (11): 8-11,43.doi: 10.13474/j.cnki.11-2246.2019.0342

土地覆盖遥感制图方法研究现状与展望

朱爽¹, 张锦水^2,3,4, 李长青¹, 郑阔¹

1. 北京工业职业技术学院, 北京 100042;
2. 地表过程与资源生态国家重点实验室, 北京师范大学地理科学学部, 北京 100875;
3. 北京市陆表遥感数据产品工程技术研究中心, 北京师范大学地理科学学部, 北京 100875;
4. 北京师范大学地理科学学部遥感科学与工程研究院, 北京 100875

收稿日期:2019-02-15 修回日期:2019-04-07 发布日期:2019-12-02
通讯作者: 张锦水。E-mail:zhangjs@bnu.edu.cn E-mail:zhangjs@bnu.edu.cn
作者简介:朱爽(1981-),女,博士,副教授,主要研究方向为环境遥感、农业遥感。E-mail:zhushuang@mail.bnu.edu.cn
基金资助:
"高分辨率对地观测系统重大专项支持项目"民用部分（09-Y20A05-9001-17/18）

Review and prospect of land cover mapping by remote sensing

ZHU Shuang¹, ZHANG Jinshui^2,3,4, LI Changqing¹, ZHENG Kuo¹

1. Beijing Polytechnic College, Beijing 100042, China;
2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;
3. Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;
4. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Received:2019-02-15 Revised:2019-04-07 Published:2019-12-02

摘要/Abstract

摘要： 区域尺度土地利用/覆盖分类研究是当今国际上开展土地利用/覆盖变化研究的重要领域之一。及时、准确地获取地球表面特性对于掌握人类和自然现象之间的关系和相互作用至关重要。本文根据土地覆盖遥感分类方法特点，从硬分类方法、软分类方法以及最新的软硬分类方法出发，总结了国内外的各研究成果，分析了各种方法的分类策略与特点及其方法适用性。研究结果表明：软硬分类方法能够灵活适用于遥感图像上纯净、混合像元并存的特点，可以有效解决光谱的异质性，在土地覆盖遥感监测中具有广阔的应用潜力。文中提出了基于变端元的软硬分类土地覆盖制图方法框架，并指出了今后的研究重点。

关键词: 硬分类方法, 软分类方法, 软硬分类方法, 变端元混合像元分解

Abstract: The study of land use/cover classification at regional scale is one of the important fields in the study of land use/cover change in the world. Accurate and timely acquisition of the characteristics of the earth's surface is essential to grasp the relationship and interaction between human and natural phenomena. Based on the characteristics of land cover remote sensing classification methods, this paper summarizes the research results here and abroad from hard classification methods, soft classification methods and the latest soft and hard classification methods, and analyses the classification strategies, characteristics and applicability of various methods. The results show that the soft and hard classification method can be flexibly applied to the characteristics of remote sensing images, such as the coexistence of pure and mixed pixels, and can effectively solve the spectral heterogeneity. It has broad application potential in remote sensing monitoring of land cover. In this paper, a framework of soft and hard classification land cover mapping method based on variable endpoints is proposed, and the research emphasis in the future is pointed out.

Key words: hard classification method, soft classification method, soft and hard classification method, mixed pixel decomposition with dynamic endmember

中图分类号:

P237

朱爽, 张锦水, 李长青, 郑阔. 土地覆盖遥感制图方法研究现状与展望[J]. 测绘通报, 2019, 0(11): 8-11,43.

ZHU Shuang, ZHANG Jinshui, LI Changqing, ZHENG Kuo. Review and prospect of land cover mapping by remote sensing[J]. Bulletin of Surveying and Mapping, 2019, 0(11): 8-11,43.

参考文献

[1] SCHAEPMAN M E. Spectrodirectional remote sensing:from pixels to processes[J]. International Journal of Applied Earth Observation and Geoinformation, 2007,9(2):204-223.
[2] 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.
[3] 张锦水, 潘耀忠, 韩立建, 等. 光谱与纹理信息复合的土地利用/覆盖变化动态监测研究[J]. 遥感学报, 2007,11(4):500-510.
[4] 朱爽, 崔有祯, 张锦水. 利用复合光谱纹理特征进行城市边缘区不透水层提取[J]. 测绘通报, 2016(11):26-30.
[5] XU S, PEDDLE D R, COBURN C A. Sensitivity of a carbon and productivity model to climatic, water, terrain, and biophysical parameters in a rocky mountain watershed[J]. Canadian Journal of Remote Sensing, 2008,34(3):245-258.
[6] PACHECO A, MCNAIRN H. Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping[J]. Remote Sensing of Environment, 2010,114(10):2219-2228.
[7] KATRA I, LANCASTER N. Surface-sediment dynamics in a dust source from spaceborne multispectral thermal infrared data[J]. Remote Sensing of Environment, 2008,112(7):3212-3221.
[8] 朱爽, 张锦水. 农作物遥感变化检测识别研究进展[J]. 中国农业资源与区划, 2015(7):159-168.
[9] ZHU S, ZHANG J S, SHUAI G Y, et al. Support vector domain description model to map specific land cover with optimal parameters determined from window-based validation set[C]//2017 IEEE International Geoscience and Remote Sensing Symposium.[S.l.]:IEEE, 2017:4774-4777.
[10] SOMERS B, ASNER G P, TITS L, et al. Endmember variability in spectral mixture analysis:a review[J]. Remote Sensing of Environment, 2011,115(7):1603-1616.
[11] POWELL R L, ROBERTS D A, DENNISON P E, et al. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis:manaus, Brazil[J]. Remote Sensing of Environment, 2007,106(2):253-267.
[12] WANG L, JIA X. Integration of soft and hard classifications using extended support vector machines[J]. Geoscience and Remote Sensing Letters, 2009,6(3):543-547.
[13] 胡潭高, 潘耀忠, 张锦水, 等. 基于线性光谱模型和支撑向量机的软硬分类方法[J]. 光谱学与光谱分析, 2011,31(2):508-511.
[14] 张锦水, 何春阳, 潘耀忠, 等. 基于SVM的多源信息复合的高空间分辨率遥感数据分类研究[J]. 遥感学报, 2006,10(1):49-57.
[15] 朱爽, 张锦水. 样本特征对参数/非参数分类器分类精度的影响分析[J]. 遥感技术与应用, 2016(4):748-755.
[16] MOUNTRAKIS G, IM J, OGOLE C. Support vector machines in remote sensing:a review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011,66(3):247-259.
[17] FOODY G M, MATURE A. The use of small training sets containing mixed pixels for accurate hard image classification:training on mixed spectral responses for classification by a SVM[J]. Remote Sensing of Environment, 2006, 103(2):179-189.
[18] SONG X F, DUAN Z J, JIANG X G. Comparison of artificial neural networks and support vector machine classifiers for land cover classification in northern China using a SPOT-5 HRG image[J]. International Journal of Remote Sensing, 2012,33(10):3301-3320.
[19] GALLEGO F J. Remote sensing and land cover area estimation[J]. International Journal of Remote Sensing, 2004,25(15):3019-3047.
[20] ZHANG L,DU B,ZHONG Y. Hybrid detectors based on selective endmembers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(6):2633-2646.
[21] 王凯, 赵军, 朱国锋, 等. 基于GF-1遥感数据决策树与混合像元分解模型的冬小麦种植面积早期估算[J]. 遥感技术与应用, 2018,33(1):158-167.
[22] 赵英时. 遥感应用分析原理与方法[M]. 北京:科学出版社, 2003.
[23] CHO M A, DEBBA P, MATHIEU R, et al. Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach:canopy-level analysis[J]. IEEE Transactions on Geoscience and Remote Sensing 2010,48(11):4133-4142.
[24] 李慧, 张金区, 曹阳, 等. 端元可变非线性混合像元分解模型[J]. 测绘学报, 2016,45(1):80-86.
[25] 吴柯, 张良培, 李平湘. 一种端元变化的神经网络混合像元分解方法[J]. 遥感学报, 2007,11(1):20-26.
[26] VILLA A, CHANUSSOT J, BENEDIKTSSON J A, et al. Spectral unmixing for the classification of hyperspectral images at a finer spatial resolution[J]. IEEE Journals of Selected Topics in Signal Processing, 2011,5(3):521-533.

土地覆盖遥感制图方法研究现状与展望

Review and prospect of land cover mapping by remote sensing

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价