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1、1.21.研究区及数据32.研究方法 面向对象的城市地物信息提取方法主要包括个步骤：影像分割、分类以及分类后处理。 进行多尺度分割，利用监督法分割精度评价方法选出各类地物的最佳分割尺度，进行尺度综合获得符合地物边界的分割结果，从而基于分割对象提取航空影像上的光谱、纹理、空间特征及LIDAR 数据中的高程、强度等特征，结合 ReliefF特征重要度进行特征选择，最后以多分类器组合方法进行分类，并进行分类后处理。42.1数据预处理 首先使用Terrascan将Lidar数据去除粗差，然后采用ENVI Lidar生成DTM，波段计算得到nDSM(nDSMDSMDTM)，它记录了所有高于地面物体(如建

2、筑物、植被等)的高程信息。然后将点云和影像配准，最后将正射影像及nDSM进行波段合成。2.2多尺度分割及尺度综合 本文采用多尺度分水岭影像分割算法，分割尺度设定为387（尺度间隔3），尺度为90以上基本合并为大区域，不予考虑，最终得到28个分割尺度。再利用监督法分割精度评价选择各类地物的最佳分割尺度，并综合最优尺度，得到分割结果。 准确度p：对象与分割对象的交集面积与分割对象面积的比值查全率r：则为交集面积与参考对象面积的比值m2：为交集面积与并集面积的比值5如图5所示的分割质量曲线图，其中建设用地包括建筑物、道路、空地；林地包括树木及草地。从图中可看出随着分割尺度的增大，p值减少，r值增加，

3、过分割现象减少，欠分割现象增多，而 m2为这两种度量的加权和，不存在偏向性。因此最佳分割尺度一般取m2最大值处，若 m2近似则选取p值较大处，得出各类地物类型的最佳分割尺度。树木与草地为33，道路与空地为45，阴影为18。62.3对象分类分类前，首先对每类地物选取其典型训练样本，统计对象光谱及指数特征、纹理特征、形状特征、对象高程信息、强度信息等40多种特征，如表1所示。本文根据ReliefF指数进行特征重要性度量，顺序选取前12项特征及面积、紧凑度（用来区分道路与空地）进行影像分类。7 特征选择后采用两步法提取城市地物，首先采用多分类器组合(K近邻算法、神经元网络及SVM_RBF)对城市地物

4、进行分类，得到如图6所示的分类结果。由于城市地区存在高层建筑及树木，分类受阴影的影响较大，因此定本文在得出初步分类结果后采用易康5.0将阴影区域进行分割，并基于规则将阴影区域依次分为：阴影下植被、阴影下建筑物、阴影下道路、阴影下空地、纯阴影5类。 图7为A区最终分类结果，利用混淆矩阵法进行分类精度评价，总体精度为93.1%，如表2所示 892.4 方法检验为检验该方法的适用性，将该方法运用于B区，得到B区分类结果，如图8、图9所示。将其进行分类精度评价，如表3所示。实验结果证明该方法能在保证分类精度的前提下，推广到类似研究区，有效提高分类效率，具有较高的适用性，为大范围城市地物信息快速提取提供

5、了可能。103 结束语 本文结合航拍影像与Lidar数据，利用监督法分割精度评价方法从多尺度分割结果中选择城市典型地物最佳分割尺度，并进行尺度综合，能快速得到符合地物边界的最优分割结果。在提取高维影像及Lidar数据的垂直结构特征基础上，利用ReliefF特征重要度度量方法选择最优特征组合，并结合多分类器组合方法进行城市地物信息提取，取得了理想的分类效果。 基于监督法分割精度评价可以定量分析地物的最佳分割尺度，减少了主观判断错误；基于ReliefF算法计算特征重要度则可以根据重要度选择分类特征，自动化程度提高；通过建立规则进行分类对象的后处理将阴影区域进行细分，能有效降低阴影产生的分类误差，该

6、方法在城市地物信息提取中具有较强的适用性，值得推广。不足：分割数据中nDSM高程误差不可避免，由于光谱特征与高程特征相同，对于植被而言，无法准确提取低矮树木的轮廓，低矮数目与草地无法分离，道路与空地分离效果也不是很好，需要进一步研究。1112.Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining. Landsat TM/ETM,unmanned aerial vehicle (UAV), and infrared thermal imager were employed

7、 to monitor underground coal fires in the Majiliangmining area. The thermal field distributions of this area in 2000, 2002, 2006, 2007, and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires. Through UAV imagery e

8、mployed at a very high resolution (0.2 m), the texture information, linear features, and brightness of the ground fissures in the coal fire area were determined. All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.

9、An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.13The Datong coalfield is located in northern Shanxi Province, China approximately from 3952 to 4010no

10、rth latitude and 1124932 to 113930 west longitude。 It has a complex terrain with an average altitude of more than 1200m. The studied area is located in the Majiliang mining area Coal fires mainly occur in the No. 2 Jurassic coal seams in the mining area, 30m to 130m below the surface, with an averag

11、e thickness of about 1m。2 Studied area143 Multi-source remote sensing monitoringthe study was conducted on different scales：1) Using the Landsat thermal infrared band, the temperature filed distribution of the entire study area was established and the whole area was classified according to its tempe

12、rature. This procedure helped to determine roughly where the underground coal fires exist. 2) Using UAV technology, a high-resolution (0.2 m) image of the studied area was obtained. Based on the characteristics of the ground fissures caused by underground coal fire, a new method was introduced to mo

13、nitor underground coal fires. 3) For areas in which the underground coal fire was serious, a thermal infrared imager was employed to build a high-precision regional surface temperature distribution.The spatial variability of the temperature distribution was analyzed to determine the location under w

14、hich points coal fires are burning.151）At present, the main algorithms used in surface temperature retrieval include the radiative transfer equation, the mono-window algorithm, and thesingle-channel method。2）Landsat ETM imageries in 2000 and 2002 and Landsat TM imageries in 2006, 2007, and 2009 were

15、 selected to obtain the surface temperature in each period (Fig. 2).Figure 2(a) shows the true color composite image of the studied area on September 25, 2002 at a resolution of 15m. The western part of the studied area is mainly composed of buildings, so that the corresponding areas in Figs. 2(b)(f

16、) show red or yellow colors, which represent high surface temperature.The high temperature parts show a southwest to northeast trend, where the temperature can be higher than the surrounding low temperature areas by around 10C to 30C. Such trend suggests that coal fires are burning underground。Lands

17、at images help roughly determine target regions where underground coal fires exist.16171819According to the texture information, linear feature,and brightness of the ground fissures, a knowledge model was established to facilitate the automatic extraction of ground fissures. The steps are as follows

18、. 1)Occurrence-based variance, cooccurrence based variance, data range, and contrast filters are used for the UAV image to obtain the texture information in the fissured area by a moving window. 基于方差的发生与共生？2) Principle component analysis (PCA) and Fisher linear discrimination(线性判别分析) analysis are th

19、en performed to extract the linear features of the area. 3) A gray level statistic is then made for the UAV image to obtain gray value samples of real fissures recorded by GPS. These characteristics are compared linear features of the area.4) Based on the model established in Steps 1 to 3, ground fi

20、ssures are automatically extracted using ERDAS software. Figure 5 shows a flowchart of the ground fissure extraction using a UAV image and Fig. 6 shows the results.(b) Extracted fissuresFig. 6 203.3.1 Surface temperature field analysis of spontaneous combustion mountainsIn this study, a Th9100 Wri8.

21、5 infrared thermal imager was used in implementing ground thermal infrared monitoring through a top-down parallel partition from the summit of the coal spontaneous combustion mountain and in collecting thermal infrared photos. The collected thermal infrared images were stitched in door according to

22、their spatial coordinates. The results are shown in Fig7.As Fig8 shows, the surface temperature in the coal fire region of the Majiliang coal mining area exhibits a strong regularity in its spatial variation. The distribution of its high-temperature and low-temperature zones is relatively concentrat

23、ed. The spatial distribution of the mountain surface temperature shows an obvious aggregation, which indicates that deep and shallow layers of coal combustion zone exist underground.21 As the figures show, the temperature in the cracks is higher than that on the surrounding surface (Fig9). However,

24、the temperature around the cracks is far lower than that around non-cracks, which shows that the cracks indeed serve as channels of air exchange between the atmosphere and the coal combustion zone inside the mountain. The internal gases are discharged through the cracks, but they do not constitute a

25、 vertical form of the internal combustion point on the surface.22 As such, no one general model can solve the problem, based on the study of the heat productionheat dissipation balance calculation equation of the spontaneous combustion depth of the inter gangue hill.This study modifies the equation

26、to estimate depth of the ignition point of the underground coal fire.Considering that the studied area is mainly filled with coal combustion, the equation was modified by canceling the gangue carbon content correction value.The new coal combustion depth calculation equation is as follows: L=g(tfta)/

27、(KKD ) According to the value of each parameter, the results are as follows. When the surface temperature is the highest (101.3 C), the burning depth of the underground coal fires is about 1.87 m. When the surface temperature is 0 C, the burning depth of the underground coal fires is about 7.33 m. W

28、hen the surface temperature is equal to or lower than the air temperature,no spontaneous combustion phenomenon exists.23 On this basis, the logarithmic and power functions with a relatively high fitness are selected to fit the functions of the spontaneous combustion temperature and the depth of unde

29、rground coal fire point, as shown in Fig10. The depth map of the internal spontaneous combustion point is shown in Fig11. First, in the graph, the white region surrounded by the blue region is the region with the shallowest ignition depth. The white region surrounded by the red region is the region with the deepest ignition depth. Second, the calculation of underground coal combustion depth is c