小波的SAR和光学图像融合方法比较研究

本文通过光学图像和SAR图像的融合,对几种典型的小波融合方法进行了分析对比实验,并使用各种评价标准对它们的融合效果给出了评价。在此基础上,对光学图像和SAR图像融合效果的各种评价标准做出了讨论并提出自己的看法。     

综合物探方法在地质灾害调查中的应用研究

介绍了在地质灾害调查中常用的几种物探方法的适用条件和应用范围,并通过实例展示了各种方法的应用效果.结果表明浅层物探方法应用效果显著,在地质灾害调查中具有较好的应用前景.     

基于MODIS数据的水稻种植面积提取研究进展

概述了水稻种植面积监测遥感数据源的应用变化、特征指数和时相选取以及遥感分类方法的发展,分析了MODIS影像在水稻种植面积遥感提取技术方面的研究进展及发展方向。结果表明：MODIS具有高光谱、高时间分辨率和多时相等特点,在大尺度上提取水稻种植面积上,可提高作物识别和监测的精确度与工作效率,节约成本,有着其他遥感数据无法相比的优势,应用MODIS数据提取水稻种植面积,取得了较好的效果。水稻遥感的最佳时相可以选择移栽期和孕穗期,利用对水体和植被较为敏感的波段或植被指数（如NDVI、LSWI和EVI）进行水稻识别,并提取种植面积。传统的遥感图像分类方法如监督分类和非监督分类,算法成熟、操作简单,是目前应用较多的方法。近年来发展起来的分类新方法,如决策树分类法、专家系统分类法、神经网络分类法,支持向量机法等,能够更准确地提取目标地物,对图像分类有不同程度的改进,在实际应用中通常和传统分类方法结合起来使用;多时相分析法与高时间、高分辨率多光谱影像的结合可以获取较高精度的作物种植面积数据,与传统分类方法相比有较大提高。利用MODIS对单一的或大面积的水稻种植面积提取效果较好,但对于地块破碎的种植面积估算尚难达到满意的结果,添加其他的辅佐数据如高程、坡度等,并结合MODIS数据的多时相特点分类等方法,可提高遥感影像分类的精度。     

基于VRMap平台上的电子沙盘研制

通过与国内外数字沙盘制作方式及功能的比较，详细介绍了数字沙盘研究方面的一些方法，并以中原油田数字沙盘的研制为例，选择北京灵图公司的VRMap2．3软件作为平台制作中原油田数字沙盘，为油田开发提供基本资料。     

腾格里沙漠地区盐湖资源调查遥感应用研究

通过对腾格里沙漠地区遥感影像信息分析与野外实地调查,圈定出大小盐湖122个,查明了盐湖的空间分布规律,划分了图兰泰湖-头道湖、白碱湖-西硝池、苏武山湖-汤家海、鸡龙同古-和屯、通古楼诺尔-巴彦达来五个盐湖分布区,对各分布区盐湖的成因类型、沉积特征、成矿规律及资源状况进行了研究,为进一步勘查、开发和利用本区资源提供了依据。     

应用AMSU资料监测中国地区雪盖的初步研究

根据微波遥感积雪的原理, 提出了利用NOAA-16 AMSU的1通道 (23.8 GHz)、2通道 (31.4 GHz) 和15通道 (89.0 GHz) 等3个通道监测中国五大区域雪盖范围的初步判识方法。由该方法制作合成了2001年10月1日至2002年4月1日中国地区的周积雪覆盖监测图, 与来自全国气象台站的地面积雪观测记录以及美国人机交互式冰雪制图系统 (I nteractive Snow and Ice Mapping System, IMS) 的雪盖图、SSM/ I (Special Sensor Microwave Imager) 雪盖图的比较结果表明:AMSU监测结果不仅具有比较高的准确性, 而且其准确性和可靠性达到甚至优于其它两种积雪监测结果 (尤其是雪季中期)。     

Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used.     

基于复数小波变换的遥感图像融合新算法

提出了一种基于复数小波变换的影像融合新算法，用以融合全色影像和多光谱影像。试验结果表明，谊算法可以取得比传统的实数小波变换融合算法更好的效果。     

Satellite image analysis using crowdsourcing data for collaborative mapping: current and opportunities

ABSTRACT

Researchers are continually finding new applications of satellite images because of the growing number of high-resolution images with wide spatial coverage. However, the cost of these images is sometimes high, and their temporal resolution is relatively coarse. Crowdsourcing is an increasingly common source of data that takes advantage of local stakeholder knowledge and that provides a higher frequency of data. The complementarity of these two data sources suggests there is great potential for mutually beneficial integration. Unfortunately, there are still important gaps in crowdsourced satellite image analysis by means of crowdsourcing in areas such as land cover classification and emergency management. In this paper, we summarize recent efforts, and discuss the challenges and prospects of satellite image analysis for geospatial applications using crowdsourcing. Crowdsourcing can be used to improve satellite image analysis and satellite images can be used to organize crowdsourced efforts for collaborative mapping.