航空遥感影像正射纠正集群并行算法

正射纠正是生成正射影像的关键步骤.随着航空遥感影像数据量的急剧增加,正射纠正的处理速度问题越来越受到关注.并行处理是解决正射纠正速度问题的有效途径.在对遥感影像正射纠正算法进行分析的基础上,重点对并行正射纠正算法中的数据划分,负载平衡等关键问题进行深入研究,并提出一种适合遥感影像快速正射纠正的负载平衡并行算法.在同构集群环境下的实验结果表明,该算法能够实现集群处理节点基本的负载平衡,达到良好的并行加速比,适合干航空遥感影像的快速正射纠正.     

改进的OPTA算法在遥感影像细化中的应用

在分析和研究快速细化算法和OPTA细化算法基础上,针对快速细化算法细化不彻底和OPTA算法模板设计的缺点提出了对OPTA细化算法的改进,设计了新的细化算法模板。经过实验证明改进的OPTA细化算法能够满足细化的基本要求,既保证了细化结果线条的单像素宽,又保持了原有图像线条的连通性,同时线条细节特征没有丢失,使细化结果得到了较大改善。     

利用MapReduce进行批量遥感影像瓦片金字塔构建

在分析面向瓦片金字塔并行构建任务分解的基础上,提出了一种利用MapReduce进行批量遥感影像瓦片金字塔并行构建的方法.实验结果表明,该方法不仅在集群上快速、有效地解决了单机上难以解决的大规模批量遥感影像瓦片金字塔的构建操作,而且具有良好的可扩展性.同时,该算法可作为大规模遥感影像并行处理的基础框架,非常容易扩展到高效能影像特征提取、遥感影像融合以及影像增量计算等其他海量遥感影像处理任务中.     

集群式遥感影像处理的关键技术

海量遥感影像快速处理是当前遥感影像处理与分析的重要任务之一。以高性能集群并行处理技术和大规模分布式处理技术为代表的遥感影像处理方法,因其能够满足遥感影像大规模快速处理任务的需求而备受关注。从计算机硬件技术、多任务处理技术、集群调度等多个方面对集群式遥感影像处理中的关键技术进行了分析与说明。     

无人机影像SIFT特征匹配的集群并行处理方法

简要介绍了国内外摄影测量并行处理技术发展的情况。在分析SIFT(尺度不变特征变换)算法和集群并行算法特点的基础上,设计了一种无人机影像匹配并行处理的方法;兼顾计算局部化和负载均衡提出了分层列划分的数据划分策略;并通过重叠通信和计算来减少通信开销,借助时间分布图对算法进行了量化分析。实验证明该方法可扩展性较好,适合在集群平台上进行影像匹配高效处理。     

基于并行编程计算模型的索贝尔滤波技术

随着遥感影像数据量的骤增,单机环境下完成索贝尔边缘滤波运算所需的计算时间也剧增.根据遥感数据的分幅特征,结合MapReduce并行分布式计算模型,本文提出了一种将该运算迁徙到Hadoop集群环境中的方法,以完成海量影像数据的索贝尔滤波运算.实验结果表明集群运算能够显著缩短计算时间,并且该计算时间会随着集群节点数目的增加而趋于减少.     

海量影像并行处理技术效率测试——以地理国情普查影像处理为例

摘要：地理国情普查项目实施过程中有大量的航空、航天遥感影像需要处理,如何高效、快捷、准确地处理这些种类繁多、形式各异的海量数据,成为地理国情普查面临的新的技术挑战。引进集群计算机并行处理海量遥感影像技术,通过测试、分析与对比,总结海量数据集群处理系统的优势。     

多源遥感影像管理与服务平台的设计与实现

针对遥感影像管理与共享服务,在分析不同来源遥感影像数据资源特点基础上,提出了适合多源、海量遥感影像数据管理的"文件+数据库"的存储和管理方式,采用镶嵌数据集进行遥感影像数据管理,并进行服务发布;同时提出了多源遥感影像管理与服务平台的总体构架体系,包括海量遥感影像管理平台和多源遥感影像网络化服务平台的设计与实现。     

集群环境下的影像并行匹配算法

遥感影像的快速匹配是3维信息实时获取的关键,而采用并行处理技术能够有效地解决匹配的速度问题.在对基于概率松弛的匹配算法进行分析的基础上,结合集群并行计算平台的特点,从负载平衡和数据通信两个方面出发,对并行匹配算法中的数据划分关键问题进行了深入讨论,提出一种负载平衡的并行匹配算法.实验结果证明,该算法能够达到计算节点的负载平衡,通信开销更小,适合在集群计算平台进行高性能影像匹配处理.     

可扩展的迭代自组织分析并行处理算法

随着遥感影像数据量的增加,传统非监督分类迭代自组织分析(ISODATA)算法的运算将十分耗时,应用并行计算技术能够有效解决该性能瓶颈。针对现有基于并行计算模型MapReduce的遥感迭代自组织分析并行算法存在的局限性,提出一种可扩展的基于MapReduce的迭代自组织分析并行处理算法。该算法通过其包含的全局子采样算法、聚类中心点集合过滤算法以及聚类映射算法,有效克服了现有并行算法中存在的不足。实验结果表明,在同等规模遥感计算中,该算法效率高于现有并行处理算法,具有良好的加速比,且在处理更大的影像块时具有更高的精度。     

A New Approach to Improve the Cluster-based Parallel Processing Efficiency of High-Resolution Remotely Sensed Image

Object-oriented remotely sensed images processing method has been accepted by more and more experts of remote sensing. To advance the efficiency of data processing, parallel image computing is a good choice since large volumes of data need be analyzed efficiently and rapidly. This paper presents the information extraction method based on per-parcel extraction of high-resolution remotely sensed image; to extract efficiently different information from remotely sensed image, this paper gives the research idea of image rough-classification based on large-scale and subtle-segmentation based on small-scale; to improve the efficiency of image processing, we adapt parallel computing method to solve this problem by presenting an new data-partition method. At last this paper gives the implementation of the research idea based on Message Passing Interface (MPI) and analyzes our experimental system efficiency, and the results show that the new methods can improve the efficiency of high-resolution remotely sensed image data processing efficiently and have a good application.     

基于Web Services的遥感图像分布式处理

遥感影像获取手段的多样化导致了遥感影像数据量的大幅度增加,应用分布式环境处理遥感图像变得越来越迫切。本文针对B/S或C/S模式在遥感影像分布式处理方面的弊端,提出了在.Net环境下基于Web Serv-ices实现遥感图像的分布式处理方案,对其框架、流程进行了设计,并以图像处理的算法分布式为例详细介绍了边缘提取中拉普拉斯8邻域提取的具体Web Services实现。结果表明,新模型下的系统在多用户并发访问等方面都有很大的改善。     

An improved SVM model for relevance feedback in remote sensing image retrieval

With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched, the global volume of remotely sensed imagery has been growing exponentially. Processing the variety of remotely sensed data has increasingly been complex and difficult. It is also hard to efficiently and intelligently retrieve what users need from a massive database of images. This paper introduces an improved support vector machine (SVM) model, which optimizes the model parameters and selects the feature subset based on the particle swarm optimization (PSO) method and genetic algorithm (GA) for remote sensing image retrieval. The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID, PSO, and GA in terms of consistency and stability.     

基于MATLAB的遥感图像变化检测

主要讨论了遥感图像变化检测的图像几何配准和阈值选取理论,利用MATLAB强大的数值计算功能实现了遥感图像变化检测.在拓展数学符号计算软件包MATLAB应用领域的同时,探索了一种遥感图像处理软件的快速开发方式.     

Parallel computing solutions for Markov chain spatial sequential simulation of categorical fields

The Markov chain random field (MCRF) model is a spatial statistical approach for modeling categorical spatial variables in multiple dimensions. However, this approach tends to be computationally costly when dealing with large data sets because of its sequential simulation processes. Therefore, improving its computational efficiency is necessary in order to run this model on larger sizes of spatial data. In this study, we suggested four parallel computing solutions by using both central processing unit (CPU) and graphics processing unit (GPU) for executing the sequential simulation algorithm of the MCRF model, and compared them with the nonparallel computing solution on computation time spent for a land cover post-classification. The four parallel computing solutions are: (1) multicore processor parallel computing (MP), (2) parallel computing by GPU-accelerated nearest neighbor searching (GNNS), (3) MP with GPU-accelerated nearest neighbor searching (MP-GNNS), and (4) parallel computing by GPU-accelerated approximation and GPU-accelerated nearest neighbor searching (GA-GNNS). Experimental results indicated that all of the four parallel computing solutions are at least 1.8× faster than the nonparallel solution. Particularly, the GA-GNNS solution with 512 threads per block is around 83× faster than the nonparallel solution when conducting a land cover post-classification with a remotely sensed image of 1000?×?1000 pixels.     

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

In high-resolution remote sensing image processing, segmentation is a crucial step that extracts information within the object-based image analysis framework. Because of its robustness, mean-shift segmentation algorithms are widely used in the field of image segmentation. However, the traditional implementation of these methods cannot process large volumes of images rapidly under limited computing resources. Currently, parallel computing models are generally employed for segmentation tasks with massive remote sensing images. This paper presents a parallel implementation of the mean-shift segmentation algorithm based on an analysis of the principle and characteristics of this technique. To avoid the inconsistency on the boundaries of adjacent data chunks, we propose a novel buffer-zone-based data-partitioning strategy. Employing the proposed data-partitioning strategy, two intensively computation steps are performed in parallel on different data chunks. The experimental results show that the proposed algorithm effectively improves the computing efficiency of image segmentation in a parallel computing environment. Furthermore, they demonstrate the practicality of massive image segmentation when computer resources are limited.     

遥感大数据智能解译的地理学认知模型与方法

随着遥感数据和计算机算力的爆炸式增长、智能分析算法瓶颈的突破,亟须提升与之相匹配的遥感大数据处理与分析能力。针对复杂场景下遥感大数据智能处理与地理学认知耦合关联和交叉融合的关键问题,本文分析了遥感大数据与地理科学各自的特点与相互关系,提出了多模态知识融合关联的深度网络构建和面向地理制图的遥感智能解译思路,建立了遥感大数据智能处理与应用体系框架;面向技术发展和行业应用,本文提出了分别建设通用高分辨率遥感智能处理系统和智能精准应用平台的总体路线,以期推动遥感智能解译技术创新和工程化应用的全面发展。     

Impact of discretization methods on the rough set-based classification of remotely sensed images

Abstract

In recent years, the rough set (RS) method has been in common use for remote-sensing classification, which provides one of the techniques of information extraction for Digital Earth. The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification. Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification. To assess the performance of discretization methods this article adopts three indicators, which are the compression capability indicator (CCI), consistency indicator (CI), and number of the cut points (NCP). An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods. To investigate the effectiveness of our method, this article applies three discretization methods of the Entropy/MDL, Naive, and SemiNaive to a TM image and three indicators for these discretization methods are then calculated. After comparing the three indicators and the classification accuracies of the discretized remotely sensed images, it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.     

改进的P-SVM支持向量机与遥感数据分类

本文介绍了将P-SVM算法引入多光谱/高分辨率遥感数据的分类, 并且展示了卫星ASTER和航空ADS40数字影像分类的技术过程和结果验证。结果表明：P-SVM方法的分类精度不低于SVM, 并减少了时耗。