基于支撑向量机概率输出的高光谱影像混合像元分解

提出利用支撑向量机（SVM）后验概率来分解高光谱影像的混合像元，通过支撑向量机的输出值转化为两两配对的后验概率，再由两两配对的概率值求得多类后验概率，并以像元所属类别的后验概率作为地物的组分信息。实验结果表明，该方法能较好地估计出混合像元的组分比。     

多暗像元大气校正方法

 针对Landsat TM影像，设计了一种适用于非均质大气的大气校正方法。该方法以暗目标减法（DOS）为基础，结合大气辐射传输模型，通过选取TM影像上多个暗像元，并计算其所对应的大气校正系数对整幅图像进行大气校正。该方法不依赖任何外部信息，并且考虑了大气的非均质性，校正精度较高。     

重力异常三维反演——视密度成像方法技术的应用

将起伏地形上测量的重力异常,延拓至平均水平面;对平面上的重力场进行不同深度层源的切割分离;再将各层的重力场向下延拓至相应的深度,并反演出该深度层的密度结构.以此得到的密度,反映该深度层密度的近似分布,故称为视密度成像.在重力场的曲面向下延拓过程中,采用了新的延拓方法——迭代法,延拓深度大,延拓过程稳定;且不需解线性代数方程组,避开了制约三维反演实用化的计算时间的瓶颈问题.在主频1.73 GHz的微机上,完成71×81数据点反演的计算时间为10 s.以新疆一个地区的实例说明这种方法的反演效果.     

Irrigated area determination: a case study in the Province of San Juan,Argentina

Much of the central-western region of Argentina, where San Juan Province is located, experiences arid to semi-arid climatic conditions with low average annual rainfall accompanied by substantial evapotranspiration. Consequently, a viable crop industry depends to a large extent upon irrigation from major river systems. Increasing demand for water in the lower basin of the San Juan River is emphasizing the need for more accurate estimates of water used for irrigation. Since the water demand for a particular crop is very closely related to crop area, monitoring the area of crop under irrigation is considered a proxy for the amount of water used. Landsat 5 imagery for the growing season, field data and aerial photographs were used to evaluate crop area.     

基于CART集成学习的城市不透水层百分比遥感估算

利用Landsat ETM^＋遥感数据，提出了一种基于CART集成学习的ISP遥感亚像元估算方法，将Boosting重采样技术引入CART分析中，用于提高ISP估算的精度。实验结果表明，该方法的ISP估算性能优于传统的单一CART学习算法，从ETM^＋影像中估算的ISP值与真实值之间的相关系数达到0．91，平均偏差为11．16％。     

一种多/高光谱遥感图像端元提取的凸锥分析算法

凸锥分析方法常用于多光谱和高光谱遥感图像的端元提取。遥感图像中的每个像元都可以看作一个多维向量,整幅影像看作由离散的非负向量构成的凸锥,通过寻找凸锥的角点来自动获取图像的端元。本文提出了一种自动选择最佳凸锥角点的方法,应用到传统的凸锥分析方法中,提高了凸锥分析方法的效率。利用模拟数据和真实数据实验验证了算法的可行性。     

Image Sources for Informal Settlement Management

Informal settlements are a common feature of developing countries. In South Africa the improvement of living conditions in these settlements and the upgrading to formal housing types are regarded as being of central importance to the nation's development. Effective settlement improvement and upgrading activities, which we here term "management", require adequate spatial data. To date, the acquisition of spatial models of informal settlements has been based on conventional mapping techniques, and mostly on photogrammetry. Data are compiled using analogue or analytical methods. These are manual and hence require both considerable expertise and expensive equipment. Moreover, these methods are uneconomical over the often relatively small, densely populated areas covered by informal settlements and are also too expensive to employ with a regularity required to support such tasks as change detection. Alternative imaging sources and mapping techniques are therefore needed.
In this article we examine the problem of spatial information acquisition for informal settlement management from three perspectives: spatial information requirements, the role which imagery can play in satisfying these spatial information requirements, and effective imaging options. We focus on the potential of high resolution satellite imaging, small format digital aerial imagery and digital multispectral video systems for rapid mapping. We also discuss the example of automated shack extraction from aerial imagery.     

Some applications of humidity profiles estimated from INSAT infra red digital cloud imagery data

Moisture profiles have been estimated over the region bounded by the latitudes 40°N and 40°S and longitudes 30°E to 130°E using INSAT digital infra red cloud imagery data. The representativeness of these profiles in representing moisture field associated with the development and movement of synoptic scale systems during the period September 15th, 1996 to March 31st, 1997 has been examined. It has been shown that the changes in the moisture field associated with the withdrawal of the southwest and northeast monsoons from the Indian sub-continent, development and movement of synoptic scale sytems (depressions, tropical cyclones and waves in easterlies) and equatorial troughs in the Indian Ocean could be clearly seen in humidity profiles. The initial development of tropical systems is first seen in the humidity field in the upper troposphere. These profiles could be used in monitoring the initial development and subsequent movement of tropical systems. Further the data on moisture distribution from the data gap regions of the Indian Ocean could be used as an additional source of moisture in numerical analysis and prognosis.