An evaluation of the role played by remote sensing technology following the World Trade Center attack

Remote sensing technology has been widely recognized for contributing to emergency response efforts after the World Trade Center attack on September 11th, 2001. The need to coordinate activities in the midst of a dense, yet relatively small area, made the combination of imagery and mapped data strategically useful. This paper reviews the role played by aerial photography, satellite imagery, and LIDAR data at Ground Zero. It examines how emergency managers utilized these datasets, and identifies significant problems that were encountered. It goes on to explore additional ways in which imagery could have been used, while presenting recommendations for more effective use in future disasters and Homeland Security applications. To plan adequately for future events, it was important to capture knowledge from individuals who responded to the World Trade Center attack. In recognition, interviews with key emergency management and geographic information system (GIS) personnel provide the basis of this paper. Successful techniques should not be forgotten, or serious problems dismissed. Although widely used after September 11th, it is important to recognize that with better planning, remote sensing and GIS could have played an even greater role. Together with a data acquisition timeline, an expanded discussion of these issues is available in the MCEER/NSF report “Emergency Response in the Wake of the World Trade Center Attack; The Remote Sensing Perspective” (Huyck and Adams, 2002)     

Using unmanned underwater vehicles as research platforms in coastal ocean studies

The advantages of using unmanned underwater vehicles in coastal ocean studies are emphasized. Two types of representative vehicles, remotely operated vehicle (ROV) and autonomous underwater vehicle (AUV) from University of South Florida, are discussed. Two individual modular sensor packages designed and tested for these platforms and field measurement results are also presented. The bottom classification and albedo package, BCAP, provides fast and accurate estimates of bottom albedos, along with other parameters such as in-water remote sensing reflectance. The real-time ocean bottom optical topographer, ROBOT, reveals high-resolution 3-dimentional bottom topography for target identification. Field data and results from recent Coastal Benthic Optical Properties field campaign, 1999 and 2000, are presented. Advantages and limitations of these vehicles and applications of modular sensor packages are compared and discussed.     

多项式的井间地震速度场反演方法

基于契比雪夫多项式的旅行时反演方法,不需进行入射角扫描,首先用多项式表示出深度和慢度,计算出从源点到接收点的旅行时间,通过与实际时间的误差修改模型参数,经过反复迭代,最终得到地下复杂构造的界面形态和速度变化。与网格化速度反演方法相比,计算的未知数少,对初始地质模型的依赖性小。模型试算表明,在初始地质模型与实际地质模型差别较大时,仍能取得比较好的反演结果。     

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.     

A Comparison between a Modified Counter Propagation Network and an Extended Self-Organizing Map in Remotely Sensed Data Classification

A modified counter propagation network model and an extended self-organizing map model have the same three-layer network architecture while employing slightly different learning rules. Their network architecture comprises an input layer, a Kohonen layer and an output layer. The neurons between two neighboring layers are fully connected and the neighboring neurons within the Kohonen layer also have neighborhood connections. The modified counter propagation network model employs the Kohonen algorithm to train the Kohonen layer while using the Widrow–Hoff rule to train the output layer. However, the extended self-organizing map model applies a modified Kohonen’s learning rule to train both the Kohonen layer and the output layer. This paper compares the performances of these two models in supervised classification of remotely sensed data. The training results show that compared to the extended self-organizing map model, the modified counter propagation model has faster learning speed but larger output errors. The classification results indicate that the extended self-organizing map model has a faster classification speed and a much higher classification precision than the modified counter propagation model.     

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.