基于统计混合模型的遥感影像阴影检测

为提高阴影检测精度,提出一种新的遥感影像阴影检测方法—将径向基函数神经网络构建的混合模型(称作SMM-RBFNN)应用于遥感影像阴影检测。灰度共生矩阵中的能量、熵、对比度和逆差矩4种统计特征量作为混合模型的输入特征矢量,采用类“期望-最大化”算法(类EM)进行参数估计,训练检测器实现阴影检测。对多幅带有浓厚阴影的遥感影像进行实验,结果表明所提出的方法明显优于传统的高斯背景法和直方图阈值法,能够较好地解决强反射性地物漏检和水体错检问题,能够克服基于阈值思想的检测法需要反复实验选取阈值的缺点。     

基于LiDAR高度纹理和神经网络的地物分类

使用LiDAR单一数据进行点云分割工作时,基于斜率的严格分割LiDAR点云的方法不能很好的适应复杂地物 的分类工作。本文将LiDAR粗分割后的点云转换为高度图像和反射强度图像,并求取高度图像GLCM高度纹理。将4 种GLCM高度纹理、地面粗糙系数、平均高度和平均反射强度共7种纹理作为识别地面覆盖物的特征,并利用后向传播 神经网络(BP-ANN)方法对LiDAR数据进行地物识别。实验表明,这种方法能够从LiDAR独立数据源中有效的实现地 物分类,实验获得的精度大于90%。与传统的最大似然法进行对比,BP-ANN的分类精度高于最大似然法。当预设地 面类型能同时满足被光学影像和LiDAR数据识别的条件时,LiDAR高度纹理分类与光学影像分类结果的一致性达到 76.5%。     

Remote gully erosion mapping using aster data and geomorphologic analysis in the Main Ethiopian Rift

The Main Ethiopian Rift(MER)is an area of extreme topography underlain by post-Miocene volcanic rocks,Jurassic limestone and a Precambrian basement.A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land.We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer(ASTER)data and geomorphologic parameters to discern patterns and features of gully erosion in the MER.Maximum Likelihood Classifica-tion(MLC),Support Vector Machine(SVM),and Minimum Distance(MD)classifiers are used to extract different gully shapes and patterns.Several spatial textures based on Grey Level Co-occurrence Matrices(GLCMs)are then generated.Afterwards,the same classifiers are applied to the ASTER data combined with the spatial texture information.We used geomorphologic parameters ex-tracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies’ shapes.The classifications show accuracies varying between 67% and 89%.Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening.This study reveals the utility of combining ASTER data and spatial textural infor-mation in discerning areas affected by gully erosion.     

Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features

Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.     

遥感资料在SCS模型产流计算中的应用

SCS模型在降雨径流关系上,考虑了土壤类型、土地利用和前期土壤含水量状况,使产流计算建立在较合理的基础上。但由于反映上述因素的综合指标CN的地区性、前期土壤含水量分级太粗、土壤分类的不通用性等,在我国直接应用误差太大。本文分析了误差太大的原因,提出了对SCS模型产流计算的改进方法;并采用土壤质地分类,提出用混合聚类法进行土壤分类。改进后的SCS模型产流计算不仅提高了精度,还可用来进行降雨径流预报。     

The Zn-Pb deposits of Casario (Ligurian Alps,NW Italy): Late Palaeozoic sedimentary-exhalative bodies affected by the alpine metamorphism

In the Ligurian Alps (South-Western Italian Alps), Zn-Pb deposits occur within late Palaeozoic meta-sedimentary units belonging to the Briançonnais Zone near Casario (Tanaro valley). Different types of sulphide-rich, lens-shaped mineralizations are recognized: sphalerite-galena massive sulphide bodies, pyrite-rich lenses and sulphide-rich quartz–carbonate-chloritoid granofels. Sulphide lenses and host rocks are affected by at least three ductile deformation phases and by a polyphase alpine metamorphism, whose climax conditions are estimated, based on P-T pseudosection calculations, at T = 300-325 °C and P = 0.55-0.60 GPa. In all the mineralized lenses the ore minerals are represented, in variable amount, by Fe-poor sphalerite, galena, pyrite and arsenopyrite (± tetrahedrite, chalcopyrite and pyrrhotite); the gangue consists of quartz, carbonate (sideritemagnesite ± rhodochrosite s.s.), Fe-chloritoid, muscovite-phengite and chlorite. The mineralizations are associated with chloritoid – carbonate micaschists displaying a finely bedded texture, with sharp between-bed compositional contrast, which suggests their exhalative origin.

In spite of the tectono-metamorphic overprint, some pre-metamorphic features of the hydrothermal system are still recognized, like relics of the hydrothermal feeding system, primary growth textures and sulphide-rich microbreccias. These massive sulphide lenses, which share many characters with the SEDEX deposits, testify to the occurrence of an exhalative event of Upper Carboniferous age previously unrecognized in the Ligurian Briançonnais Unit.     

哈拉达拉辉长岩以及其中磁铁矿-辉石脉的岩石学和地球化学研究

西南天山哈拉达拉杂岩体主要由橄长岩、橄榄辉长岩和辉长岩组成,岩浆通过早期橄榄石和斜长石结晶分离后,残余岩浆侵位形成了辉长岩.辉长岩中单斜辉石(Cpx-Ⅰ)的稀土元素含量低(REE ＜51 × 10-6),Eu负异常不明显,其他微量元素(如Sc、Cr)的含量和Mg#值(80～ 95)变化大(透辉石-普通辉石).磁铁矿-辉石脉穿切辉长岩,其中单斜辉石(Cpx-Ⅱ)的成分比较均一(透辉石).与Cpx-Ⅰ相比,Cpx-Ⅱ的稀土元素含量明显高(REE＞ 100× 10-6),且Eu负异常非常显著.与晚期富Fe基性岩浆的快速侵位不同,辉长岩的形成经历了复杂的演化过程.Cpx-Ⅰ和Cpx-Ⅱ均发育出溶结构,表明晚期富Fe基性岩浆快速侵位形成磁铁矿-辉石脉之后,哈拉达拉岩体或者经历了快速抬升(或者快速降温)的过程.岩浆过程结束后,低温热演活动形成了含金方解石-磁铁矿脉.研究这种热液脉,对本地区开展金矿找矿勘探有重要意义.     

基于Contourlet变换的高空间分辨率遥感影像检索研究

本文在高空间分辨率遥感影像纹理特征提取研究中引入Contourlet变换,将不同尺度、不同方向子带系数矩阵的均值、方差和能量作为纹理特征,并为分类能力强的特征量赋予较大的权值,体现相应子带分类能力的差异性。以QuickBird影像为样本数据、采用五叉树分解策略的纹理检索实证研究表明,基于Contourlet变换提取高空间分辨率遥感影像纹理特征信息的效果优于Gabor小波变换。     

SAR image classification based on its texture features

SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation. It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.