多源遥感影像湿地检测概率潜在语义分析

doi:10.11947/j.AGCS.2017.20160292

测绘学报 ›› 2017, Vol. 46 ›› Issue (8): 1017-1025.doi: 10.11947/j.AGCS.2017.20160292

多源遥感影像湿地检测概率潜在语义分析

许凯¹, 张倩倩¹, 王彦华¹, 刘福江¹, 秦昆²

1. 中国地质大学(武汉)信息工程学院, 湖北武汉 430074;
2. 武汉大学遥感信息工程学院, 湖北武汉 430079

收稿日期:2016-06-15 修回日期:2017-07-03 出版日期:2017-08-20 发布日期:2017-09-01
通讯作者: 张倩倩 E-mail:zqqian_cug@163.com
作者简介:许凯(1983-),男,博士,讲师,研究方向为遥感图像处理的理论与方法。E-mail:xukai_cug@163.com.
基金资助:
国家重点研发计划（2016YFB0502603）；地理国情监测国家测绘地理信息局重点实验室开放基金（2016NGCM09）

Wetland Detection from Multi-sources Remote Sensing Images Based on Probabilistic Latent Semantic Analysis

XU Kai¹, ZHANG Qianqian¹, WANG Yanhua¹, LIU Fujiang¹, QIN Kun²

1. Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China;
2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Received:2016-06-15 Revised:2017-07-03 Online:2017-08-20 Published:2017-09-01
Supported by:
National Key Research and Development Program of China (No. 2016YFB0502603);Key Laboratory for National Geographic State Monitoring of National Administration of Surveying, Mapping and Geoinformation (No. 2016NGCM09)

摘要/Abstract

摘要： 提出了一种基于概率潜在语义分析的多源遥感影像湿地检测方法。首先提取高分辨率影像的光谱、纹理和湿地场景的地物组成成分，并结合由多光谱遥感数据提取的湿地地表温度、土壤含水量，组成湿地场景的特征空间；然后利用概率潜在语义分析将湿地场景表示成多个潜在语义的组合，并用潜在语义的权值向量来描述湿地场景的特征空间；最后利用SVM分类器实现湿地场景的检测。试验表明，概率潜在语义分析能够将湿地的高维特征空间映射到低维的潜在语义空间中，地物组成成分和定量环境特征的加入能更加有效地表征湿地特征空间，提高湿地检测精度。

关键词: 概率潜在语义分析, 湿地检测, 语义信息, 多源遥感

Abstract: A novel wetland detection approach for multi-sources remote sensing images was proposed, which based on the probabilistic latent semantic analysis (pLSA). Firstly, spectral, texture, and subclass of wetland were extracted from high-resolution remote sensing image, and land surface temperature and soil moisture of wetland were derived from corresponding multispectral remote sensing image. The feature space of wetland scene was hence formed. Then, wetland scene was represented as a combination of several latent semantics using pLSA, and the feature space of the wetland scene was further described by weight vector of latent semantics. Finally, supporting vector machine (SVM) classifier was applied to detect the wetland scene. Experiments indicated that the adoption of pLSA is able to map the high-dimensional feature space of wetland to low-dimensional latent semantic space. Besides, the addition of subclass and quantitative environment features is able to characterize wetland feature space more effectively and improve the detection accuracy significantly.

Key words: probabilistic latent semantic analysis, wetland detection, semantic information, multi-sources remote sensing

中图分类号:

P237

许凯, 张倩倩, 王彦华, 刘福江, 秦昆. 多源遥感影像湿地检测概率潜在语义分析[J]. 测绘学报, 2017, 46(8): 1017-1025.

XU Kai, ZHANG Qianqian, WANG Yanhua, LIU Fujiang, QIN Kun. Wetland Detection from Multi-sources Remote Sensing Images Based on Probabilistic Latent Semantic Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(8): 1017-1025.

参考文献

[1] 李玉凤, 刘红玉. 湿地分类和湿地景观分类研究进展[J]. 湿地科学, 2014, 12(1):102-108. LI Yufeng, LIU Hongyu. Advance in Wetland Classification and Wetland Landscape Classification Researches[J]. Wetland Science, 2014, 12(1):102-108.
[2] 曹宇, 莫利江, 李艳, 等. 湿地景观生态分类研究进展[J]. 应用生态学报, 2009, 20(12):3084-3092. CAO Yu, MO Lijiang, LI Yan, et al. Wetland Landscape Ecological Classification:Research Progress[J]. China Journal of Applied Ecology, 2009, 20(12):3084-3092.
[3] 李建平, 张柏, 张泠, 等. 湿地遥感检测研究现状与展望[J]. 地理科学进展, 2007, 26(1):33-43. LI Jianping, ZHANG Bai, ZHANG Ling, et al. Current Status and Prospect of Researches on Wetland Monitoring Based on Remote Sensing[J]. Progress in Geography, 2007, 26(1):33-43.
[4] FROHN R C, D'AMICO E, LANE C, et al. Multi-temporal Sub-pixel LandSat ETM+Classification of Isolated Wetlands in Cuyahoga County, Ohio, USA[J]. Wetlands, 2012, 32(2):289-299.
[5] 江冲亚, 李满春, 刘永学. 海岸带水体遥感信息全自动提取方法[J]. 测绘学报, 2011, 40(3):332-337. JIANG Chongya, LI Manchun, LIU Yongxue. Full-automatic Method for Coastal Water Information Extraction from Remote Sensing Image[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(3):332-337.
[6] SOSNOWSKI A, GHONEIM E, BURKE J J, et al. Remote Regions, Remote Data:A Spatial Investigation of Precipitation, Dynamic Land Covers, and Conflict in the Sudd Wetland of South Sudan[J]. Applied Geography, 2016, 69:51-64.
[7] HAN Xingxing, CHEN Xiaoling, FENG Lian. Four Decades of Winter Wetland Changes in Poyang Lake Based on LandSat Observations between 1973 and 2013[J]. Remote Sensing of Environment, 2015, 156:426-437.
[8] OZESMI S L, BAUER M E. Satellite Remote Sensing of Wetlands[J]. Wetlands Ecology and Management, 2002, 10(5):381-402.
[9] HOUHOULIS P F,MICHENER W K. Detecting Wetland Change:A Rule-based Approach Using NWI and SPOT-XS Data[J]. Photogrammetric Engineering and Remote Sensing, 2000, 66(2):205-211.
[10] JOLLINEAU M Y, HOWARTH P J. Mapping an Inland Wetland Complex Using Hyperspectral Imagery[J]. International Journal of Remote Sensing, 2008, 29(12):3609-3631.
[11] LI Junhua, CHEN Wenjun. A Rule-based Method for Mapping Canada's Wetlands Using Optical, Radar and DEM Data[J]. International Journal of Remote Sensing, 2005, 26(22):5051-5069.
[12] 李慧. 基于多源遥感数据的湿地信息提取及景观格局研究-以闽江河口区湿地为例[D]. 福州:福建师范大学, 2005. LI Hui. Wetland Information Extraction Based on Multi-source RS Data and Landscape Pattern Analysis:A Case of Minjiang River Estuary[D]. Fuzhou:Fujian Normal University, 2005.
[13] WRIGHT C, GALLANT A. Improved Wetland Remote Sensing in Yellowstone National Park Using Classification Trees to Combine TM Imagery and Ancillary Environmental Data[J]. Remote Sensing of Environment, 2007, 107(4):582-605.
[14] KINDSCHER K, FRASER A, JAKUBAUSKAS M E, et al. Identifying Wetland Meadows in Grand Teton National Park Using Remote Sensing and Average Wetland Values[J]. Wetlands Ecology and Management, 1997, 5(4):265-273.
[15] HOFMANN T. Unsupervised Learning by Probabilistic Latent Semantic Analysis[J]. Machine Learning, 2001, 42(1-2):177-196.
[16] 周晖, 郭军, 朱长仁, 等. 引入PLSA模型的光学遥感图像舰船检测[J]. 遥感学报, 2010, 14(4):663-680. ZHOU Hui, GUO Jun, ZHU Changren, et al. Ship Detection from Optical Remote Sensing Images Based on PLSA Model[J]. Journal of Remote Sensing, 2010, 14(4):663-680.
[17] 陶超, 谭毅华, 彭碧发, 等. 一种基于概率潜在语义模型的高分辨率遥感影像分类方法[J]. 测绘学报, 2011, 40(2):156-162. TAO Chao,TAN Yihua,PENG Bifa, et al.A Probabilistic Latent Semantic Analysis Based Classification for High Resolution Remotely Sensed Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(2):156-162.
[18] WANG Yong,MEI Tao,GONG Shaogang,et al.Combining Global, Regional and Contextual Features for Automatic Image Annotation[J]. Pattern Recognition, 2009, 42(2):259-266.
[19] OJALA T, PIETIKINEN M, HARWOOD D. A Comparative Study of Texture Measures with Classification Based on Featured Distributions[J]. Pattern Recognition, 1996, 29(1):51-59.
[20] OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987.
[21] 胡德勇, 乔琨, 王兴玲, 等. 单窗算法结合LandSat 8热红外数据反演地表温度[J]. 遥感学报, 2015, 19(6):964-976. HU Deyong, QIAO Kun, WANG Xingling, et al. Land Surface Temperature Retrieval from LandSat 8 Thermal Infrared Data Using Mono-window Algorithm[J]. Journal of Remote Sensing, 2015, 19(6):964-976.
[22] CONGALTON R G. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data[J]. Remote Sensing of Environment, 1991, 37(1):35-46.
[23] FOODY G M.Status of Land Cover Classification Accuracy Assessment[J]. Remote Sensing of Environment, 2002, 80(1):185-201.
[24] QUELHAS P, MONAY F, ODOBEZ J M, et al. A Thousand Words in a Scene[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(9):1575-1589.

多源遥感影像湿地检测概率潜在语义分析

Wetland Detection from Multi-sources Remote Sensing Images Based on Probabilistic Latent Semantic Analysis

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