基于粗糙集的K--均值聚类算法在遥感影像分割中的应用

结合粗糙集理论和K——均值聚类算法，提出一种遥感影像的粗糙聚类分割方法。根据遥感影像中特征属性的相互依赖关系，应用粗糙集理论的等价关系。求出K——均值聚类所需要的初始类的个数和均值。然后采用聚类算法对图像进行分割。实验结果表明该方法比随机选取聚类的中心点和个数减少了运算量．提高了分类精度和准确性。     

Role of the Kazerun fault system in active deformation of the Zagros fold-and-thrust belt (Iran)

Field structural and SPOT image analyses document the kinematic framework enhancing transfer of strike-slip partitioned motion from along the backstop to the interior of the Zagros fold-and-thrust belt in a context of plate convergence slight obliquity. Transfer occurs by slip on the north-trending right-lateral Kazerun Fault System (KFS) that connects to the Main Recent Fault, a major northwest-trending dextral fault partitioning oblique convergence at the rear of the belt. The KFS formed by three fault zones ended by bent orogen-parallel thrusts allows slip from along the Main Recent Fault to become distributed by transfer to longitudinal thrusts and folds. To cite this article: C. Authemayou et al., C. R. Geoscience 337 (2005).     

渤海湾西岸全新世地层的分段与划组

本文以岩性地层、气候地层(CaCO3含量、古温度和Rb/Sr比值等)、年代地层、沉积化学地层和生物组合地层等资料为基础,根据地层规范以及建组要求将渤海湾西岸全新世地层划分为3个组:独流镇组( Qh3 dl)、大黄洼组(Qh2dh)和西翟庄组(Qh1xz).其地层埋深界线及测年界线分别为:晚全新世独流镇组(Qh3dl)与...     

Links between NAO fluctuations and inter-annual variability of winter-months precipitation in the Seine River watershed (north-western France)

The inter-annual to multi-decadal winter variability (DJFM) of precipitation on the Seine River watershed (France) was analysed using continuous wavelet transform analysis and compared to the winter North Atlantic Oscillation Index (NAOI). Nine weather stations were used over the 1951 to 2004 period and confirmed the homogeneity of inter-annual fluctuations for all stations but one. Wavelet coherence between SLP over the Icelandic and Azores regions and precipitation highlighted coherence for different scales of variability according to the centre of action considered. Segmentation and wavelet analysis and coherence between precipitation and NAOI over a long period of time (1873–2004) showed: i) increasing variability across the last century at most time scales, especially for NAOI; ii) the existence of change points for the mean and variance of both signals; iii) overall discontinuity of the coherence whatever the scale considered, especially between ∼1910 and ∼1955 for inter-decennial to pluri-decennial scales.     

汶川8级大地震的地表破裂特征及分段

2008年5月12日14时28分,四川省汶川县境内发生MS8.0地震.地表破裂多以跌水、陡坎形态发育在河流沟谷或晚新生代沉积层内,位移明显.山地受崩塌、滑坡影响,位移量较难获得.发震断裂主要有三条,即北川-映秀断裂、彭县-灌县断裂和小鱼洞断裂.北川-映秀断裂地表破裂由南向北活动性质从逆冲为主逐渐转变为走滑为主,长约220 km,平均垂直位移量约3 m,按位移量沿断裂走向的变化可以分为虹口段、北川段和南坝段;彭县-灌县断裂地表破裂以逆冲活动为主,长约82 km,平均垂直位移量约1.5 m,可以划分为白鹿段和汉旺段,断裂断距分布的几何特征与北川-映秀断裂的中南段相近;小鱼洞断裂是一条新生北西走向的次级破裂,长约5.6 km,平均垂直位移量约1.5 m,调节两侧构造单元变形差异,具有捩断层特征,活动以逆(左行)走滑为主,可划分出小鱼洞段和中坝段.姚都镇地表破裂可能说明南坝以北的地震具有不同的活动特征.活动断裂的运动方式反映区域应力场有北西西向挤压特征.     

豫西三门峡地区温塘断裂第四纪活动性研究

温塘断裂位于鄂尔多斯块体东南缘,是三门峡盆地的东边界控制断裂,准确厘定其活动性对于理解区域构造演化和判断三门峡地区地震危险性具有重要意义。运用野外地质地貌调查、联合钻孔剖面探测及光释光定年技术,对三门峡地区温塘断裂的活动性进行分析与研究。结果表明,温塘断裂南段出露地表,线性特征明显,断层最新活动时代为晚更新世;温塘断裂中段为第四纪隐伏断裂,断层活动时代为中更新世;温塘断裂北段断层出露,断层活动时代为中更新世。     

活断层运动区段的划分问题

断层运动是复杂而多变的,在一条巨大的断裂带上经常以区段为单位活动,每次活动的区段长度、运动量等都不相同,且往往具有一定的迁移规律等。为工程建设和强震的预测,划分活断层运动区段越来越迫切。以一次强震活动序列、跨断层大地测量网的复测、直接鉴定断层新活动、并结合地形地貌和地下水位变化等途径来划分断层运动的区段     

标记分水岭算法及区域合并的遥感图像分割

传统的分水岭算法通常对梯度图像进行无标记分割，其结果是容易造成过度分割。本文采用了一种基于标记的分水岭算法，首先,利用Sobel边缘算子对原遥感图像进行梯度重建，获得梯度幅值图像，同时计算待分割区域的周长、面积和形态因子，并对其进行标记； 然后,利用距离函数图标定种子法和等值线跟踪法获得初始分割图像； 最后，利用改进的区域合并方法获得最终的分割结果。实验结果表明了该方法的有效性。     

Stability of Kerogen Classification with Regard to Image Segmentation

This paper investigates the stability of an automatic system for classifying kerogen material from images of sieved rock samples. The system comprises four stages: image acquisition, background removal, segmentation, and classification of the segmented kerogen pieces as either inertinite or vitrinite. Depending upon a segmentation parameter d, called “overlap”, touching pieces of kerogen may be split differently. The aim of this study is to establish how robust the classification result is to variations of the segmentation parameter. There are two issues that pose difficulties in carrying out an experiment. First, even a trained professional may be uncertain when distinguishing between isolated pieces of inertinite and vitrinite, extracted from transmitted-light microscope images. Second, because manual labelling of large amount of data for training the system is an arduous task, we acquired the true labels (ground truth) only for the pieces obtained at overlap d=0.5. To construct ground truth for various values of d we propose here label-inheritance trees. With thus estimated ground truth, an experiment was carried out to evaluate the robustness of the system to changes in the segmentation through varying the overlap value d. The average system accuracy across values of d spanning the range from 0 to 1 was 86.5%, which is only slightly lower than the accuracy of the system at the design value of d=0.5 (89.07%).     

Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification

Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing.Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline.