In order to make full use of the vector data in the historical period and the temporal relationship of the feature classes, the authors propose a classification method based on temporal feature fusion for high spatial resolution remotely sensed imagery in the paper. Image objects are generated using subdivision based on the vector data in the historical period and the remotely sensed imagery in the present period. SVM algorithm is adopted to get the initial class and posterior probability with a single period of the object. Class transition probabilities for description of temporal feature are calculated according to the class of the image objects in the historical and present periods. The iterative method is employed to get the final classification results after weighted combination of the posterior probability with a single period and the transition probability of the image objects. The experiment on QuickBird imagery shows the proposed method can exploit the temporal feature effectively and improve the accuracy of the image classification.
[1] 赵红蕊,阎广建,邓小炼,等.一种简单加入空间关系的实用图像分类方法[J].遥感学报,2003,7(5):358-363. Zhao H R,Yan G J,Deng X L,et al.A classification method based on spatial information[J].Journal of Remote Sensing,2003,7(5):358-363.[2] 曹凯,江南,吕恒,等.面向对象的SPOT 5影像城区水体信息提取研究[J].国土资源遥感,2007,19(2):27-30.doi:10.6046/gtzyyg.2007.02.07. Cao K,Jiang N,Lyu H,et al.The extraction of water information in urban areas based on SPOT 5 image using object-oriented method[J].Remote Sensing for Land and Resources,2007,19(2):27-30.doi:10.6046/gtzyyg.2007.02.07.[3] 孙永军,童庆禧,秦其明.利用面向对象方法提取湿地信息[J].国土资源遥感,2008,20(1):79-82.doi:10.6046/gtzyyg.2008.01.18. Sun Y J,Tong Q X,Qin Q M.The object-oriented method for wetland information extraction[J].Remote Sensing for Land and Resources,2008,20(1):79-82.doi:10.6046/gtzyyg.2008.01.18.[4] 何春阳,陈晋,陈云浩,等.土地利用/覆盖变化混合动态监测方法研究[J].自然资源学报,2001,16(3):255-262. He C Y,Chen J,Chen Y H,et al.Land use/cover change detection based on hybrid method[J].Journal of Natural Resources,2001,16(3):255-262.[5] 邓媛媛,巫兆聪,易俐娜,等.面向对象的高分辨率影像农用地分类[J].国土资源遥感,2010,22(4):117-121.doi:10.6046/gtzyyg.2010.04.24. Deng Y Y,Wu Z C,Yi L N,et al.Research on object-oriented classification of agricultural land based on high resolution images[J].Remote Sensing for Land and Resources,2010,22(4):117-121.doi:10.6046/gtzyyg.2010.04.24.[6] 王琰,舒宁,龚龑,等.基于类别光谱变化规律的土地利用变化检测[J].国土资源遥感,2012,24(3):92-96.doi:10.6046/gtzyyg.2012.03.17. Wang Y,Shu N,Gong Y,et al.Land use change detection based on class spectral change rule[J].Remote Sensing for Land and Resources,2012,24(3):92-96.doi:10.6046/gtzyyg.2012.03.17.[7] 吴波,朱勤东,高海燕,等.面向对象影像分类中基于最大化互信息的特征选择[J].国土资源遥感,2009,21(3):30-34.doi:10.6046/gtzyyg.2009.03.06. Wu B,Zhu Q D,Gao H Y,et al.Feature selection based on maximal mutual information criterion in object-oriented classification[J].Remote Sensing for Land and Resources,2009,21(3):30-34.doi:10.6046/gtzyyg.2009.03.06.[8] Blaschke T,Hay G J.Object-oriented image analysis and scale-space:Theory and methods for modeling and evaluating multiscale landscape structures[J].International Archives of Photogrammetry and Remote Sensing,2001,34(4/W5):22-29.[9] Walter V.Object-based classification of remote sensing data for change detection[J].ISPRS Journal of Photogrammetry and Remote Sensing,2004,58(3/4):225-238.[10] 白穆,刘慧平,乔瑜,等.高分辨率遥感图像分类方法在LUCC中的研究进展[J].国土资源遥感,2010,22(1):19-23.doi:10.6046/gtzyyg.2010.01.03. Bai M,Liu H P,Qiao Y,et al.New progress in the classification of high spatial resolution satellite images for LUCC[J].Remote Sensing for Land and Resources,2010,22(1):19-23.doi:10.6046/gtzyyg.2010.01.03.[11] Tang Y Q,Zhang L P,Huang X.Object-oriented change detection based on the Kolmogorov-Smirnov test using high-resolution multispectral imagery[J].International Journal of Remote Sensing,2011,32(20):5719-5740.[12] Benz U C,Hofmann P,Willhauck G,et al.Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[J].ISPRS Journal of Photogrammetry and Remote Sensing,2004,58(3/4):239-258.[13] 张倩,黄昕,张良培.多尺度同质区域提取的高分辨率遥感影像分类研究[J].武汉大学学报:信息科学版,2011,36(1):117-121. Zhang Q,Huang X,Zhang L P.Multiscale image segmentation and classification with supervised ECHO of high spatial resolution remotely sensed imagery[J].Geomatics and Information Science of Wuhan University,2011,36(1):117-121.[14] Zhao Y D,Zhang L P,Li P X,et al.Classification of high spatial resolution imagery using improved gaussian markov random-field-based texture features[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(5):1458-1468.[15] 万幼川,黄俊.几何和图论特征对高分辨率遥感影像土地利用分类的影响[J].武汉大学学报:信息科学版,2009,34(7):794-798. Wan Y C,Huang J.Influence of geometric and graph theoretical measures on land classification using high-resolution remote sensing images[J].Geomatics and Information Science of Wuhan University,2009,34(7):794-798.[16] 乔程,沈占锋,吴宁,等.空间邻接支持下的遥感影像分类[J].遥感学报,2011,15(1):88-99. Qiao C,Shen Z F,Wu N,et al.Remote sensing image classification method supported by spatial adjacency[J].Journal of Remote Sensing,2011,15(1):88-99.[17] 蔡银桥,毛政元.基于多特征对象的高分辨率遥感影像分类方法及其应用[J].国土资源遥感,2007,19(1):77-81.doi:10.6046/gtzyyg.2007.01.17. Cai Y Q,Mao Z Y.A method for classification of high resolution remotely sensed images based on multi-feature objects and its application[J].Remote Sensing for Land and Resources,2007,19(1):77-81.doi:10.6046/gtzyyg.2007.01.17.[18] 李雪,舒宁,王琰,等.利用土地利用状态转移分析的变化检测[J].武汉大学学报:信息科学版,2011,36(8):952-955. Li X,Shu N,Wang Y,et al.Change detection based on land-use status transition analysis[J].Geomatics and Information Science of Wuhan University,2011,36(8):952-955.[19] Bruzzone L,Serpico S B.An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(4):858-867.[20] Bontemps S,Bogaert P,Titeux N,et al.An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution[J].Remote Sensing of Environment,2008,112(6):3181-3191.[21] 吴波,张良培,李平湘.基于支撑向量机概率输出的高光谱影像混合像元分解[J].武汉大学学报:信息科学版,2006,31(1):51-54. Wu B,Zhang L P,Li P X.Unmixing of hyperspectral imagery based on probabilistic outputs of support vector machines[J].Geomatics and Information Science of Wuhan University,2006,31(1):51-54.
2016, Vol. 28 
