Land Cover Classification Using ALOS Image Based on Textural Features and Support Vector Machine
LI Ling1, WANG Hong1, LIU Qing-sheng2, NING Ji-cai2
1. School of Earth Sciences and Engineering, Hehai University, Nanjing 210098, China;
2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
The high spatial resolution remote sensing images are used widely in the land cover classification; nevertheless, the traditional pixel-based classification has the weakness of relatively low accuracy. For the purpose of improving the accuracy of the high spatial resolution image classification,the textural features were extracted quickly by using the method of Gray Level Co-occurrence Matrices (GLCM),and then the ALOS image of the typical test area in Huzhou city of Zhejiang province was classified based on textural features and Support Vector Machine (SVM). The results show that image classification based on textural features and SVM can better extract surface features with precision of 90.88%. The classification precision based on SVM only is higher than that based on maximum likelihood,with the former precision being 89.96% and the latter 86.16%. Extracting land cover types quickly and accurately can provide a service for the research on appearance and spatial-temporal distribution of the agricultural non-point pollution source,and also provide scientific evidence for exploration of reasonable land use model and sustainable land utilization in Taihu basin.
李玲, 王红, 刘庆生, 宁吉才. 基于纹理特征和支持向量机的ALOS图像土地覆被分类[J]. 国土资源遥感, 2011, 23(4): 58-63.
LI Ling, WANG Hong, LIU Qing-sheng, NING Ji-cai. Land Cover Classification Using ALOS Image Based on Textural Features and Support Vector Machine. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(4): 58-63.
[1] 张维理,武淑霞,冀宏杰,等.中国农业面源污染形势估计及控制对策Ⅰ--21世纪初期中国农业面源污染的形势估计［J］.中国农业科学,2004,37(7):1008-1017.[2] 姜青香,刘慧平.利用纹理分析方法提取TM图像信息［J］.遥感学报,2004,8(5):458-464.[3] 陈启浩,高伟,刘修国.辅以纹理特征的高分辨率遥感影像分类［J］.测绘科学,2008,33(1):88-90.[4] Chavez P Jr,Bauer B.An Automatic Optimum Kernel-size Selection Technique for Edge Enhancement［J］.Remote Sensing of Environment,1982(1),12:23-38.[5] Franklin S E,McDemid G J.Empirical Relations Between Digital SPOT HRV and CASI Spectral Response and Lodgepole Pine (Pinas Contorta) Forest Stand Parameters［J］.International Jounal of Remote Sensing,1993,14(12):2331-2348.[6] Franklin S E,Wukler M A,Lavingne M B.Automated Derivation of Geographic Window Sizes for Use in Remote Sensing Digital Image Texture Analysis［J］.Computers & Geosciences,1996,22(6):665-673.[7] Curran P J.The Semivariogram in Remote Sensing:an Introduction［J］.Remote Sensing of Environment,1988,24(3):493-507.[8] Marceau D J,Howarth P J,Dubois J M,et al.Evaluation of the Grey-level Co-occurrence Matrix Method for Land-cover Classification Using SPOT Imagery［J］.IEEE Transactions on Geoscience and Remote Sensing,1990,28(4):513-519.[9] Hsu S.Texture-tone Analysis for Automated Land-use Mapping［J］.Photogrammetric Engineering & Remote Sensing,1978,44(11):1393-1404.[10] Dutra L V,Mascarenhas N D A.Some Experiments with Spatial Feature Extraction Methods in Multispectral Classification［J］.International Journal of Remote Sensing,1984,5(2):303-313.[11] 黄颖端,李培军,李争晓.基于地统计学的图像纹理在岩性分类中的应用［J］.国土资源遥感,2003(3):45-49.[12] Hallo,Hay G J,Marceau D J.Multiscale Object-specific Analysis Scale Problems and Multiscale Solutions［C］//Proceedings of 12th International Conference on Geoinfomatics-Geospatial Information Research:Bridging the Pacific and Atlantic.Sweden:Press of University of Gävele,2004.[13] 宋翠玉,李培军,杨锋杰.运用多尺度图像纹理进行城市扩展变化检测［J］.国土资源遥感,2006(3):37-42.[14] Zhu G B,Blumberg D G.Classification Using ASTER Data and SVM Algorithms:the Case Study of Beer Sheva,Israel［J］.Remote Sensing of Environment,2002,80(2):233-240.[15] Keuchel J,Naumann S,Heiler M,et al.Automatic Land Cover Analysis for Tenerife by Supervised Classification Using Remotely Sensed Data［J］.Remote Sensing of Environment,2003,86(4):530-541.[16] Nemmour H,Chibani Y.Multiple Support Vector Machines for Land Cover Change Detection:an Application for Mapping Urban Extensions［J］.ISPRS Journal of Photogrammentry and Remote Sensing,2006,61(2):125-133.[17] Inglada J.Automatic Recognition of Man-made Objects in High Resolution Optical Remote Sensing Images by SVM Classification of Geometric Image Features［J］.ISPRS Journal of Photogrammetry and Remote Sensing,2007,62(3):236-248.[18] 刘龙飞,陈云浩,李京.遥感影像纹理分析方法综述与展望［J］.遥感技术与应用,2003,18(6):441-447.[19] Haralick R M,Shanmugan K,Dinsrein I.Textural Features for Image Classification［J］.IEEE Trans actions on Man and Cybern etics,1973,3(6):610-621.[20] 冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究［J］.北京测绘,2007(3):19-22.[21] Cristianini N,Shawe-Taylon J.支持向量机导论［M］.北京:电子工业出版社,2006.[22] 张睿,马建文.改进的P-SVM支持向量机与遥感数据分类［J］.遥感学报,2009,13(3):445-452.[23] 张学工.关于统计学习理论与支持向量机［J］.自动化学报,2000,26(1):37-38.
2011, Vol. 23 
