Atmospheric water vapor plays a significant role in the study of climate change and hydrological cycle processes. In order to acquire the accurate distribution of atmospheric water vapor which is varying with time, location, and altitude, it is necessary to monitor it at high spatial and temporal resolution. Unfortunately, it is difficult to map the spatial distribution of atmospheric water vapor due to the lack of meteorological instrumentation at adequate spatial and temporal observation scales. This paper introduces a simplified method to retrieve Precipitable Water Vapor (PWV) using the ratio of the apparent reflectance values of the 18th and 19th band of Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to the EOS PWV products of the same time and area, the PWV estimated using this simplified method is closer to the radiosonde results which is considered as the true PWV value. Results reveal that this simplified method is applicable over cloud-free atmospheric conditions of the mid-latitude regions.

单兵小型无人机侦察系统是以小型无人机为载体,配备微型摄像设备进行侦察,通过小型数字化的通信设备搭建数据链将图像数据传送至地面站,单兵则可利用多功能的手持终端进行情报上报。介绍了单兵小型无人机侦察系统的组成及其工作过程,并对各部分的功能、要求和现状进行了探讨,为进一步的深入研究打下了基础。

In this paper, the SVM classifier with RBF kernel function was utilized to tackle the classification of ASTER remote sensing image. Instead of the original image, the image of Gi, which is a statistics describing the local spatial structure, is inputted to the SVM classifier to get the final classification result. The classifying process includes a "probing stage" and a "classifying stage". The objective of the "probing stage" is to find an optimal lag value of Gi; and in the "classifying stage", the Gi image with the optimal lag is classified by the SVM classifier. The experimental result shows that Gi images with appropriate lag values can be used to distinguish land covering features with similar spectral characteristics and different local spatial structures and, as a result, to improve the overall classification accuracy.

Land classification for high spatial resolution remote sensing images is an important topic in many applications.In this paper,the support vector machine(SVM) algorithm was utilized to tackle the classification of a 3-band image from airborne digital sensor system with ground resolution of 0.32 meters.Firstly,the original image was classified using SVM of four common types of kernel functions,namely linear,polynomial,RBF and sigmoid function,and the SVM with RBF kernel function can achieve the most satisfactory result with statistical overall accuracy over 91%.On the other hand,Getis-Ord Gi,one type of local spatial statistics to determine clusters of similar values,had been calculated based on the original spectral image with varying lags from 1 to 10.Classifying Gi with lag of 3 other than the original spectral image,an overall accuracy of 95.66% was achieved using SVM based on the RBF kernel function.The result of the experiment shows that Gi with lags less than the variogram range can substitute for the original spectral image to improve the classification accuracy between features with similar spectral characteristics like trees and lawns,as a result,to increase the overall classification accuracy.

When the impending antiship cruise missile's motion vector alines with the axis of IR detection system, it is difficult to detect and track it by its trace analysis. A novel spatio-temporal filtering method is presented, which employs wavelet analysis and pixel value changing measurement. The images used in the experiments are composed of some real pictures and man-made point target (signals,) and the method is proved to be with high accuracy and sensitivity and low false alarm rate.