[1] 袁修孝, 宋妍.一种运用纹理和光谱特征消除投影差影响的建筑物变化检测方法[J].武汉大学学报·信息科学版, 2007, 32(6): 489-493 doi:  10.3321/j.issn:1671-8860.2007.06.005

Yuan Xiuxiao, Song Yan. A Building Change Detection Method Considering Projection Influence Based on Spectral Feature and Texture Feature[J]. Geomatics and Information Science of Wuhan University, 2007, 32(6):489-493 doi:  10.3321/j.issn:1671-8860.2007.06.005
[2] 叶昕, 秦其明, 王俊, 等.利用高分辨率光学遥感图像检测震害损毁建筑物[J].武汉大学学报·信息科学版, 2019, 44(1):128-134 http://www.cnki.com.cn/Article/CJFDTotal-WHCH201901017.htm

Ye Xin, Qin Qiming, Wang Jun, et al. Detecting Damaged Buildings Caused by Earthquake from Remote Sensing Image Using Local Spatial Statistics Method[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1):128-134 http://www.cnki.com.cn/Article/CJFDTotal-WHCH201901017.htm
[3] Tong X, Lin X, Feng T, et al. Use of Shadows for Detection of Earthquake-Induced Collapsed Buildings in High-Resolution Satellite Imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79(5):53-67 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0357eae6c32fb6312085c536eab2396d
[4] Qin R. An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images[J]. Remote Sensing, 2014, 6(9):7 911-7 932 doi:  10.3390/rs6097911
[5] 彭代锋, 张永军, 熊小军.结合LiDAR点云和航空影像的建筑物三维变化检测[J].武汉大学学报·信息科学版, 2015, 40(4):462-468 doi:  10.13203/j.whugis20130325

Peng Daifeng, Zhang Yongjun, Xiong Xiaojun. 3D Building Change Detection by Combining LiDAR Point Clouds and Aerial Imagery[J]. Geomatics and Information Science of Wuhan University, 2015, 40(4):462-468 doi:  10.13203/j.whugis20130325
[6] Huang X, Zhang L. Morphological Building/Shadow Index for Building Extraction from High-Resolution Imagery over Urban Areas[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(1):161-172 doi:  10.1109/JSTARS.2011.2168195
[7] Huang X, Zhang L, Zhu T. Building Change Detection from Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 7(1):105-115 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=0a944f2fcaf02818b35dba01ab26cb34
[8] Hou B, Wang Y, Liu Q. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images[J]. Sensors, 2016, 16(9):1 377 doi:  10.3390/s16091377
[9] 巩翼龙, 闫利.结合机载LiDAR数据与航空可见光影像多层次规则分类建筑物变化检测[J].光谱学与光谱分析, 2015(5):1 325-1 330 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gpxygpfx201505040

Gong Yilong, Yan Li. Building Change Detection Based on Multi-Level Rules Classification with Airborne LiDAR Data and Aerial Images[J]. Spectroscopy and Spectral Analysis, 2015(5):1 325-1 330 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gpxygpfx201505040
[10] Xu Z, Mountrakis G, Quackenbush L J. Impervious Surface Extraction in Imbalanced Datasets: Integrating Partial Results and Multi-Temporal Information in an Iterative One-Class Classifier[J]. International Journal of Remote Sensing, 2017, 38(1):43-63 doi:  10.1080/01431161.2016.1259677
[11] Fauvel M, Chanussot J, Benediktsson A. A SpatialSpectral Kernel-based Approach for the Classification of Remote-Sensing Images[J]. Pattern Recognition, 2012, 45(1):381-392 http://www.sciencedirect.com/science/article/pii/S0031320311002019
[12] Chaudhuri B, Sarkar N. Texture Segmentation Using Fractal Dimension[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(1):72-77 doi:  10.1109/34.368149
[13] 贾永红, 谢志伟, 吕臻, 等.一种新的遥感影像变化检测方法[J].武汉大学学报·信息科学版, 2016, 41(8): 1 001-1 006 doi:  10.13203/j.whugis20150025

Jia Yonghong, Xie Zhiwei, Lv Zhen, et al. A New Change Detection Method of Remote Sensing Image[J]. Geomatics and Information Science of Wuhan University, 2016, 41(8):1 001-1 006 doi:  10.13203/j.whugis20150025
[14] Wu M, Ye J. A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(11):2 088 doi:  10.1109/TPAMI.2009.24
[15] Chang C, Lin C. LIBSVM: A Library for Support Vector Machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):27 http://dl.acm.org/citation.cfm?id=1961199
[16] Lin S, Lee Z, Chen C, et al. Parameter Determination of Support Vector Machine and Feature Selection Using Simulated Annealing Approach[J]. Applied Soft Computing, 2008, 8(4):1 505-1 512 doi:  10.1016/j.asoc.2007.10.012
[17] Blaschke T, Hay G, Kelly M, et al. Geographic Object-Based Image Analysis: Towards a New Paradigm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87(100):180-191 https://www.sciencedirect.com/science/article/abs/pii/S0924271613002220
[18] Ma L, Fu T, Li M. Active Learning for Object-Based Image Classification Using Predefined Training Objects[J]. International Journal of Remote Sensing, 2018, 39(9):2 746-2 765 doi:  10.1080/01431161.2018.1430398
[19] Melville B, Lucieer A, Aryal J. Object-based Random Forest Classification of Landsat ETM+ and WorldView-2 Satellite Imagery for Mapping Lowland Native Grassland Communities in Tasmania, Australia[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 66 http://adsabs.harvard.edu/abs/2018IJAEO..66...46M
[20] Hu Z, Qian Z, Qin Z, et al. Stepwise Evolution Analysis of the Region-Merging Segmentation for Scale Parameterization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 7(11):2 461-2 472 http://ieeexplore.ieee.org/document/8370056/