【摘要】：在遙感技術(shù)與相關(guān)領(lǐng)域飛速發(fā)展的今天，遙感影像的內(nèi)容信息分析已逐漸成為行業(yè)內(nèi)部研究主流，單一尺度的遙感影像分析已不能滿足對大區(qū)域預(yù)警與環(huán)境分析的要求，遙感影像鑲嵌已成為遙感影像處理中的一個(gè)熱門話題，然而遙感影像的特性與行業(yè)要求使得遙感影像鑲嵌技術(shù)復(fù)雜冗長，處理流程繁雜不易簡化，選擇正確的方式方法處理大規(guī)模大尺度遙感圖像的鑲嵌問題變得十分重要，有效利用軟硬件資源，充分分配計(jì)算資源與計(jì)算空間需要算法的支持與匹配。 針對上述情況，本文研究了圖像鑲嵌技術(shù)，并行編程技術(shù)，任務(wù)調(diào)度方式方法，并分析了解了現(xiàn)有的大尺度遙感鑲嵌算法中存在的問題，針對運(yùn)行速率低與可擴(kuò)展性差等普遍問題，提出了基于任務(wù)樹調(diào)度的大尺度遙感影像并行鑲嵌技術(shù)，，通過對大尺度遙感影像集進(jìn)行解耦，解析影像間關(guān)系，構(gòu)建具有前驅(qū)后續(xù)邏輯關(guān)系的任務(wù)樹，利用高性能集群本地資源管理器分配集群高效的運(yùn)算能力，智能調(diào)度監(jiān)控，將已觸發(fā)任務(wù)發(fā)送至就緒狀態(tài)隊(duì)列，等待空閑節(jié)點(diǎn)運(yùn)行，節(jié)點(diǎn)中的子鑲嵌任務(wù)采用MPI(Message Passing Interface)并行運(yùn)算編程方法實(shí)現(xiàn)，達(dá)到多層次并行，充分利用現(xiàn)有硬件設(shè)施的目的。 本文提出的調(diào)度方案為基于關(guān)鍵路徑和狀態(tài)隊(duì)列（CPDS-SQ）的動態(tài)DAG(Directed Acyclic Graph)調(diào)度策略，并根據(jù)此調(diào)度策略完成了并行調(diào)度TTM（Task-tree Mosaic）系統(tǒng)，動態(tài)生成任務(wù)調(diào)度序列，有效減少任務(wù)樹長度，增強(qiáng)任務(wù)并行度，大幅度縮減了并行鑲嵌的運(yùn)算時(shí)間，同時(shí)，動態(tài)調(diào)度過程中的糾錯(cuò)重發(fā)機(jī)制提高了程序的魯棒性，對大尺度遙感圖像的鑲嵌實(shí)現(xiàn)提供了良好基礎(chǔ)。 本文針對上述調(diào)度方法設(shè)計(jì)實(shí)現(xiàn)了大尺度遙感鑲嵌系統(tǒng)，詳細(xì)闡述了系統(tǒng)軟硬件的實(shí)現(xiàn)過程與結(jié)果。
[Abstract]:With the rapid development of remote sensing technology and related fields, the content information analysis of remote sensing image has gradually become the mainstream of research in the industry. The single scale remote sensing image analysis can no longer meet the requirements of regional early warning and environmental analysis. Remote sensing image mosaic has become a hot topic in remote sensing image processing. However, the characteristics and industry requirements of remote sensing image make remote sensing image mosaic technology complicated and lengthy, and the processing process is not easy to simplify. It is very important to choose the correct method to deal with the mosaic problem of large-scale and large-scale remote sensing images. The efficient use of software and hardware resources and the adequate allocation of computing resources and computing space need the support and matching of algorithms. In view of the above situation, this paper studies image mosaic technology, parallel programming technology, task scheduling method, and analyzes the existing problems in large-scale remote sensing mosaic algorithm. Aiming at the common problems such as low running rate and poor scalability, the parallel mosaic technology of large-scale remote sensing image based on task tree scheduling is proposed. By decoupling the large-scale remote sensing image set, the relationship between the images is analyzed. The task tree with the preprocessing and subsequent logic relationship is constructed, and the high performance cluster local resource manager is used to allocate the cluster's efficient computing power. The task is sent to the ready state queue to wait for the idle node to run, and the triggered task is sent to the ready state queue. The sub-mosaic task in the node is implemented by MPI (Message Passing Interface) parallel operation programming method to achieve multi-level parallelism and make full use of the existing hardware facilities. The scheduling scheme proposed in this paper is a dynamic DAG (Directed Acyclic Graph) scheduling strategy based on critical path and state queue (CPDS-SQ). According to this scheduling strategy, a parallel scheduling TTM (Task-tree Mosaic) system is implemented, which dynamically generates task scheduling sequences and effectively reduces the length of task tree. The task parallelism is enhanced and the computation time of parallel mosaic is greatly reduced. At the same time, the error correction and retransmission mechanism in dynamic scheduling improves the robustness of the program and provides a good foundation for the realization of large-scale remote sensing image mosaic. In this paper, a large scale remote sensing mosaic system is designed and implemented in accordance with the above scheduling method, and the realization process and results of the software and hardware of the system are described in detail.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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