基于ZY3数据的南水北调西线生态遥感调查研究

国产ZY3高分辨率卫星数据在国土资源管理方面已经有较广泛的应用,通过对ZY3数据的处理和信息自动提取,可获取南水北调西线工程沿线的土地利用及植被群落分布状况。通过本次遥感解译流程,克服卫星时相、区域地貌差异等问题,形成适用于ZY3卫星遥感影像生态遥感解译的流程,与传统的生态遥感解译方法相比,不仅成本低,而且工期短,可为大区域性生态遥感解译提供依据。     

LM-BP神经网络在遥感影像分类中的应用研究

有效地利用卫星遥感数据进行多类别识别并提高分类精度一直是遥感应用研究的前沿。以江苏南京江宁区为试验区,复合最佳指数提取的波段组合光谱信息、灰度共生矩阵提取的纹理信息和地理辅助数据及其派生信息,运用LM-BP神经网络实现遥感影像分类,并将分类结果与标准BP网络和传统分类方法进行了比较。研究表明,将卫星数据与地理辅助数据结合,发展多源多维信息复合的LM-BP方法可以大大提高分类的精度,是提高遥感应用性的有效途径。     

卫星遥感海冰监测技术在河北省近海海域的应用

卫星遥感技术已成为海冰监测的重要技术手段之一。介绍了应用EOS系列卫星、HY-1B卫星和HJ-1A/B卫星等遥感数据提取河北省近海海域海冰分布、海冰类型、海冰厚度等海冰信息的方法。重点研究了应用HJ-1A/B卫星遥感数据进行海冰分类,及根据海冰类型与厚度的对应关系估算海冰厚度的方法,并以2013年1月17日的卫星遥感数据为例,提取海冰信息,制作相关海冰专题产品。结果表明:利用多种卫星遥感数据进行海冰监测,既可相互补充又能提高监测效率和准确率,制作更精细化的海冰监测产品,为河北海洋管理部门制定防灾减灾措施提供重要依据。     

遥感卫星原始数据质量检查方法与实现

本文在研究了国内外多颗遥感卫星数据传输格式的基础上,提出了一种遥感卫星原始数据质量检查的快速方法,在星地对接和接收站数据质量检查工作中得到了应用。该方法利用查找表算法对原始数据中已知的信息如同步字、部分辅助数据等进行快速比对检测。采用数据分段分析的策略,实现对整轨卫星数据的检测分析,并通过直观的图表形式给出数据质检报告。作者以资源、环境减灾等卫星数据为研究对象,在软件帧同步、RS解码等方面进行了深入的研究,获得良好效果。     

基于灰度与纹理信息融合的影像分割方法

文章提出了一种融合遥感影像像元各波段灰度信息与影像纹理信息的图像分割方法, 设计并实现了一种基于四分树的区域分开-合并算法。该方法有效地利用了遥感影像像元各波段灰度信息以及影像纹理信息, 用SPOT 5 卫星数据的实验结果表明, 该方法对影像分类以及制图工作有一定的促进作用。     

基于出口遥感卫星移动接收站站监控软件设计

对于出口国际的遥感卫星移动接收站,站监控软件负责完成整站任务规划与调度、轨道预报、数据管理以及设备集中监视和控制;针对有人值守无人操作的遥感卫星移动接收站的运行特点和任务需求,制定遥感卫星数据接收优先级和任务筛选规则,按需采集地理位置信息,定时通过以太网获取最新卫星轨道数据,自动规划遥感卫星数据接收任务,利用任务节点矩阵驱动数据接收流程,自动化控制和调度移动站内各系统完成遥感卫星数据接收工作;在实际应用中提高了遥感卫星移动站的利用率,避免了人工操作降低系统运行效率,保障了遥感卫星数据接收的全覆盖,实现了有人值守无人操作的全自动化运行。     

基于相似性挖掘的遥感SST评估和订正技术

针对遥感反演精度受大气环境干扰较重,特别是云、雨,尤其是在水汽丰富海域反演数据的可靠性不易把握的特点.根据对卫星遥感温度信息的精度和信度,提出了一种相似性评判法则,即借助相似性理论将待测等温线与历史等温线比较来修正海表面温度.实验结果表明,给定方法可以提高数据反演精度,特别是遥感数据质量较差的区域效果明显.     

容差粗糙集卫星遥感数据分类

结合粗糙集理论和遥感数据中地物光谱特征空间分布信息,提出了一种基于光谱特征邻域的容差粗糙集分类方法,用来处理卫星遥感数据分类中的不确定性问题。利用北京地区Landsat-5 TM数据进行分类试验,对算法分类过程进行讨论及其分类结果进行验证分析;结果表明：文中方法在可理解性和稳定性上体现出比较好的性质,能够有效处理卫星遥感数据分类中存在的不确定性因素,在具有复杂光谱特征地物分类方面具有发展潜力。     

黑河综合遥感联合试验研究进展:水文与生态参量遥感反演与估算

介绍了“黑河综合遥感联合试验”在水文和生态变量与参数反演、估算和模型应用方面取得的进展。在水文变量遥感方面,利用车载双偏振多普勒雷达在黑河上游和中游分别开展了高精度降水观测,获取了后向散射系数和极化信息与降水强度之间的定量关系。在综合利用多源观测信息,改进和发展蒸散发估算模型方面取得了实质性的进展。发展了利用K和Ka波段机载微波辐射计数据反演山区积雪深度的方法。针对SAR观测数据反演土壤水分中地表粗糙度的显著干扰,发展了消除粗糙度影响的反演方法。在生态过程遥感参量估算方面,提出了一种基于机载激光雷达和高分辨率光学影像的高精度地物信息分类方法。发展了从高光谱航空遥感提取植被自然光照下的荧光,并与NDVI结合的C3/C4植被分类方法。发展和改进了使用多角度、多光谱观测反演叶面积指数的方法,挖掘了激光雷达在植被垂直结构探测上的潜力,探索了叶面积指数遥感中的尺度转换规律。发展了利用高光谱数据中的荧光信息反演光能利用率的新方法;建立了考虑土壤反射率、冠层结构等因素的光合作用有效辐射比率反演模型;改进了利用遥感估计生态系统生产力的模型。发展了利用高光谱遥感数据提取叶绿素含量和叶绿素荧光强度的方法。     

基于分布式集群架构的遥感数据传输机制

对地观测遥感卫星数据传输系统采用Linux虚拟服务器集群架构传输遥感卫星数据,而该集群中承载传输任务的新旧服务器在实现断点续传的过程中由于控制信息不同步会造成数据片重复。设计一种基于分布式系统架构的遥感卫星数据处理机制DPM,采用Kafka消息队列快速存储数据包,利用代码模块使Spark Streaming精准提交消息偏移量,并通过数据传输速率与进度统计实时记录DPM运行状态信息。测试结果表明,DPM机制可精准记录与提交消息偏移量并实现数据续传功能,从而确保遥感卫星数据的准确稳定传输。     

星载SAR图像中居民地专题信息增强的方法探讨

探讨了通过SAR图像中的纹理信息以增强居民地专题信息的方法。首先,采用共生矩阵的方法,对图像纹理特征进行描述,产生纹理特征波段。然后,对这些特征波段进行相关分析,并采用假彩色合成增强的方法,增强居民地的专题信息。最后得到最佳增强的纹理波段组合方案。结果表明该方法能有效地增强居民地的专题信息,从而为居民地自动提取提供可靠数据。     

A drainage system analysis evaluation of,and comparison between,Landsat-3 RBV,Landsat-5 TM and SPOT PA imageries covering the Central Macedonia district,Greece

Abstract

Landsat-3 RBV, Landsat-5 TM imageries and SPOT PA stereopair diapositives were visually interpreted for the purpose of finding the accuracy of certain morphometric variables of three drainage basin sample areas in Central Macedonia, North Greece, drawn separately from each of the above three types of satellite imageries and comparisons were made between the efficiency of drainage systems drawn from each of the above imageries and the drainage systems extracted from the available topographic maps of 1:50000 scale.

The main findings were the following: (1) SPOT PA stereopair diapositives of 1:200000 scale can be used to map drainage systems to an order of magnitude slightly more than TM imagery of 1:125000 scale, but significantly more than RBV imagery of 1:125000 scale. This slight superiority of SPOT imagery over TM imagery implies that the greater spectral range of TM, compared with the shorter range of SPOT imageries, vastly outweighs the advantage of SPOT'S superior resolution, but not the superiority of stereoscopic view; (2) TM imagery can be used to map drainage systems to an order of magnitude significantly more than RBV imagery; and (3) RBV imagery can be used to map drainage systems to an order of magnitude less than topographic maps of 1:50000 scale but better than topographic maps of 1:100000 scale.     

基于深度置信网络Otsu混合模型的自动云检测算法

地球表面一半以上被云覆盖,卫星遥感图像中的云检测主要是人工检测识别或者半自动化方法,依赖人工干预,效率不高,难以满足实时或准实时应用的需要。为了提高卫星遥感数据的可用性,基于深度置信网络（DBN）和最大类间方差法,提出一种自动云检测算法——DOHM。该算法采用自适应阈值代替人工标定阈值,实现云检测的全自动化,将云检测的正确率提高到95%以上;DOHM算法选取了维度为9的特征向量作为检测网络的输入,输入特征向量的多样性,有利于网络更全面有效地捕捉到云的特点。     

深度卷积网络卫星图像水泥厂目标检测

目的 水泥厂作为重要的污染源企业需要对其进行统计和监管,近几年随着卫星遥感技术的发展和遥感影像分辨率的提高,使得基于卫星影像进行水泥厂目标检测成为可能。但是由于遥感图像中建筑目标的环境复杂多变,同时各个水泥厂在生产规模、设备构成、厂区结构、坐落方位上存在较大差异,图像表观上的形态各异和复杂环境干扰使得传统图像识别方法难以设计和提取有效特征。鉴于深度学习在视觉目标检测领域的成功应用,本文将研究应用深度卷积神经网络方法,实现在卫星图像上识别与定位水泥厂目标,为环保部门提供一种高效便捷的水泥厂目标检测和统计方法。方法 基于面向目标检测与定位的Faster R-CNN深度学习框架,以准确检测与定位水泥厂区域为目的,以京津冀地区的水泥厂位置作为训练和测试数据集,选用3种结构不同的提取特征卷积神经网络模型进行了对比实验。并针对小样本训练容易出现的过拟合和误检问题,采用图像去雾预处理、数据扩充、引入负样本等技术进一步提升模型能力。结果 测试集实验结果表明ResNet特征提取网络效果最好,准确率达到74%。为了进一步提高检出率并降低误检率,引入3种模型能力提升方法,在扩充检测数据集中的检出率达到94%,误检率降低到14%;在全球水泥厂数据集中的图像检出率达到96%,万幅随机图像的误检数量为30幅（0.3%）。对上海地区的卫星图像进行扫描检测,结果检测出11个已登记的水泥厂（共登记16个）,另外还检测出17个未登记的水泥厂。结论 对于卫星地图上水泥厂这种具有特殊建筑构造但也存在厂区几何形状各异、所处地理环境复杂、随季节性变化等特点,本文提出的基于深度卷积网络的卫星图像水泥厂检测方法,能够自动学习提取有效的图像特征并对目标进行准确检测。针对小样本训练问题,引入3种方法显著提高了模型的检测精度。在模型泛化能力测试中,经过优化后的模型在水泥厂建筑目标检测任务中表现良好,具有重要的应用价值。     

Elevation modelling from satellite visible and infrared (VIR) data

Since the early emergence of Earth observation satellites, researchers have investigated different methods of extracting three-dimensional information using satellite data. Apart from a few early stereo images by hand-held photographs acquired during the Gemini and Apollo missions, the first experiments to extract three-dimensional data using stereo viewing from space began with the Earth Terrain Camera flown onboard SkyLab in 1973/74. Since this time, various analogue or digital sensors in the visible spectrum have flown to provide researchers and geoscientists with spatial data to extract and interpret three-dimensional information of the Earth's surface. Although clinometry techniques can be applied with the optical sensor images, stereo viewing of images was and still is the most common method used by the mapping, photogrammetry and remote sensing communities for elevation modelling. The paper will review clinometry and stereoscopy and their applicability to the different satellite sensors (space photographs and scanners). Their performances to extract absolute or relative elevation from various research and commercial organizations are addressed. The respective advantages, difficulties and constraints of the sensors are discussed, as well as the methods and the technologies used for extracting elevation data in an operational context.     

Registering real and virtual imagery

Registering a virtual scene with a real scene captured by a video camera has a number of applications including visually guided robotic navigation, surveillance, military training and operation. The fundamental problem involves several challenging research issues including finding corresponding points between the virtual and the real scene and camera calibration. This paper presents our research in defining and mapping a set of reliable image features for registering the two imageries, extracting and selecting reliable control points for the construction of intrinsic and extrinsic camera parameters. A number of innovative algorithms are presented followed by extensive experimental analysis. An application of registering virtual database image with video image is presented. The algorithms we developed for calculating and matching linear structured features and selecting of reliable control points are applicable to image registration beyond virtual and real imageries.     

基于波谱角和掩膜的卫星影像阴影去除研究

详细分析了卫星影像的阴影来源,以遮挡物类型和传感器探测特点为依据对阴影进行了分类和研究。分析了波谱角法检测阴影的技术特点和操作方式,研究了掩膜技术提取阴影的方法,并提出采用分波段线性扩展和滤波的手段去除阴影。通过ETM数据的试验分析,验证了波谱角和掩膜技术可以在去除陆地卫星影像阴影中发挥重要作用。     

Use of colour transformation and the geodesic method for road centreline extraction from VHR satellite images

Seed point-based road extraction methods are vital for extracting road networks from satellite images. Despite its effectiveness, roads in very high-resolution (VHR) satellite images are complicated, such as road occlusion and material change. To tackle this issue, this paper proposes to use the colour space transformation and geodesic method. First, the test image is converted from Red-Green-Blue colour space to Hue-Saturation-Value colour space to reduce the material change influence. The geodesic method is subsequently applied to extract initial road segments that link road seed points provided by users. At last, the initial result is adjusted by a kernel density estimation method to produce centred roads. The presented method is quantitatively evaluated on three test images. Experiments show that the proposed method yields a substantial improvement over cutting-edge technologies. The findings in this study shine new light on a practical solution for road extraction from satellite images.     

The recognition of road network from high‐resolution satellite remotely sensed data using image morphological characteristics

With the development of remote sensors and satellite technologies, high‐resolution satellite data such as IKONOS images have been available recently. By these new high‐resolution satellite data, remote sensing technologies can be successfully applied to more application areas such as extracting road network from high‐resolution satellite images. This paper proposes a newly developed approach to extract a road network from high‐resolution satellite images. The approach is based on the binary and greyscale mathematical morphology and a line segment match method. First, the outline of road network is detected based on the grey morphological characteristics. Then, the basic road network is detected by the line segment match method. Next, the detected basic road network is processed based on the knowledge about the roads and binary mathematical morphological methods. Finally, visual analysis and three indicators are used to evaluate the accuracy of the extracted road networks. The results of the accuracy evaluation demonstrate that the developed road network extraction approach can provide both good visual effect and high positional accuracy.