中巴卫星遥感资料在土地资源遥感调查中的应用

对中巴资源卫星遥感资料的处理方法和技术流程进行了分析和讨论。并以新疆国土资源环境遥感综合调查项目的土地资源遥感调查重点县尉犁县为例，利用2000年4～9月中巴资源卫星遥感资料，计算了遥感资料在土地调查中的分类精度。     

塔里木盆地周边地区冰雹云特征分析

对1998～2001年6～9月在塔里木盆地普查得到105个降雹云团进行分类，按尺度大小分为雷暴云、对流云、中尺度对流系统、冷云核和系统云系云区5类。塔里木盆地2000年降雹云团出现最多，半数以上出现在5月和6月。塔里木盆地降雹云团尺度小、形状不规则、云顶温度较高。     

北疆降雹云团卫星云图特征的分类研究

北疆降雹云团按其卫星云图特征分为对流性和系统性两大类，又将对流性降雹云团按尺度大小分为雷暴云和对流云，系统性降雹云团按所处位置分为冷云核、云系云区和云系边缘。据此方法对1998-2001年4-9月期间北疆出现310个降雹云团进行归类分析。北疆降雹云团以对流性云团为主，它们尺度小、形状不规则、云顶亮温较高。主要出现在北疆西部和北疆沿天山中段。各类降雹云团在各地区的出现机率不同。     

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.     

自组织网络在遥感土地覆盖分类中应用研究

设计完成和比较了自组织网络的几种算法在遥感土地覆盖分类中的应用,结果表明非监督和监督学习结合方法进行遥感土地覆盖分类,各算法在分类性能上无显著差异,因此可采用算法和较简单的单竞争学习网络,根据最邻近原则进行非参数分类。     

Clustering based on eigenspace transformation – CBEST for efficient classification

Large remote sensing datasets, that either cover large areas or have high spatial resolution, are often a burden of information mining for scientific studies. Here, we present an approach that conducts clustering after gray-level vector reduction. In this manner, the speed of clustering can be considerably improved. The approach features applying eigenspace transformation to the dataset followed by compressing the data in the eigenspace and storing them in coded matrices and vectors. The clustering process takes the advantage of the reduced size of the compressed data and thus reduces computational complexity. We name this approach Clustering Based on Eigen-space Transformation (CBEST). In our experiment with a subscene of Landsat Thematic Mapper (TM) imagery, CBEST was found to be able to improve speed considerably over conventional K-means as the volume of data to be clustered increases. We assessed information loss and several other factors. In addition, we evaluated the effectiveness of CBEST in mapping land cover/use with the same image that was acquired over Guangzhou City, South China and an AVIRIS hyperspectral image over Cappocanoe County, Indiana. Using reference data we assessed the accuracies for both CBEST and conventional K-means and we found that the CBEST was not negatively affected by information loss during compression in practice. We discussed potential applications of the fast clustering algorithm in dealing with large datasets in remote sensing studies.     

Mapping spiny aster infestations with QuickBird imagery

QuickBird satellite imagery acquired in June 2003 and September 2004 was evaluated for detecting the noxious weed spiny aster [Leucosyris spinosa (Benth.) Greene] on a south Texas, USA rangeland area. A subset of each of the satellite images representing a diversity of cover types was extracted and used as a study site. The satellite imagery had a spatial resolution of 2.8 m and contained 11-bit data. Unsupervised and supervised classification techniques were used to classify false colour composite (green, red, and near-infrared bands) images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster infestations. This was attributed to differences in spiny aster phenology between the two dates. An unsupervised classification of the June image showed that spiny aster had producer's and user's accuracies of 90% and 93.1%, respectively, whereas a supervised classification of the June image had producer's and user's accuracies of 90% and 81.8%, respectively. These results indicate that high resolution satellite imagery coupled with image analysis techniques can be used successfully for detecting spiny aster infestations on rangelands.     

综合危险性指数法在苍山县地质灾害易发区划分中的应用

地质灾害的发生和发展是地质环境因素和人类工程经济活动共同作用的结果，其易发性主要受自然因素和人为因素的共同影响。以山东省苍山县地质灾害易发区划分为例，探讨性地采用了地质灾害综合危险性指数法，定量划分了苍山县地质灾害易发区，结果符合当地地质灾害发育的实际情况，为苍山县地质灾害的预防和治理工作提供了科学依据。     

多级BP神经网络石油测井信号分类器

本文提出了一种利用多级ＢＰ神经网络进行石油测井信号分类的新方法．介绍了用多级ＢＰ网络处理测井信号的分类器算法和网络结构，并给出了针对理论模拟信号的分类结果及针对实际模型井信号的分类结果，其正确率可达９０％以上．     

地震活动性参数分类及其相关性初步研究

从各地震活动性参数的原始定义及其计算过程所反映出的物理本质出发，并为研究不同参数间相关性方便，将已有的30多种地震活动性参数分为五类：频度类、能量类、分布类、综合类和非线性类。在过去工作的基础上，进一步研究了同一类别中各参数间的相关性，取得了一些初步看法。