Study On Parallel Processing Technologies Of Photogrammetry Data Based On GPU

Based on GPU Cluster Platform, this thesis makes a comprehensive study on the parallel processing technologies and methods of photogrammetry algorithms to meet the rapid processing requirements of massive photogrammetry data. Its aim is to resolve the key problems of GPU Cluster construction and application, and explore the technical methods and optimization strategies of photogrammetry data processing. The main work and innovation of this paper is as follows:1. Principle of parallel computing, development history and trend of parallel computer, parallelism between multi-processing tasks, and basic parallel processing modes of photogrammetry images are briefly analyzed and summarized. And the GPU's hardware framework, software programming model, performance analysis model, optimization principle and basic strategies are discussed in detail, which provides the theoretical basis for fine granularity parallel processing through single GPU card. And moreover, two experimental platforms are given.2. Based on the degraded image radiation model in bad weather condition and Dark Channel Prior, a novel and effective haze removal method for single image is introduced, tested and analyzed. Aiming at the complex and time-consuming disadvantages of initialized atmosphere transmission's interpolating and refining, a fast and edge-preserving interpolating method is proposed based on Guided Image Filter. Based on integral image and box filter, fine granularity parallel computing through single GPU card is realized.3. A fast GPU-CPU cooperate geometric rectification algorithm is presented based on CUDA, which realizes fine granularity parallel processing of re-sampling through a single GPU card. And on the basis of GPU's hardware framework and software programming model, three performance optimization strategies are proposed to make full use of GPU's high parallel computing advantages: using reasonable task partition and executing scheme to increase GPU threads'Warp occupancy; using high bandwidth shared memory optimization technology to reduce accessing times of coordinate transform coefficients in low-speed global memory; replacing global memory with texture memory to reduce the original image's accessing time.4. Through the analysis of the True Ortho-photo generation flow and existing occluded area detecting methods, a fast occlusion detecting method based on shadow-testing technology is proposed. And based on Z-Pass algorithm, GPU hardware accelerating method is realized through Stencil Buffer and Depth Buffer of 3D pipeline to draw the occluded areas.5. After analyzing the SURF (Speeded-Up Robust Features) detecting, describing and matching principle, a corresponding fine granularity parallel processing method through a single GPU card is proposed. A reasonable thread organizing scheme and Aomic Calculation of GPU is used to ensure the correctness of detected SURF points. A partition computing pattern is used in intensive matrix computing of feature matching, false matches filtering and relative orientation elements computing to make full use of GPU's parallel computing advantagethrough reducing accessing times to low bandwidth global memory.6. After analyzing the parallelism of dense gray level matching, a GPU fine granularity parallel processing method is proposed. And some optimization strategies are discussed in detail, which include accessing optimization of matching searching zone data, parallel processing granularity of correlate matching, GPU thread organizing scheme of cost aggregation step in semi-global matching. Moreover, multi-baseline matching mode and parallel processing method is further discussed.7. The logical framework, hardware and software configuration scheme and corresponding process flow of the loose couping GPU Cluster is designed. Based on reasonable coarse granularity task decomposing schemes and efficient task organizing and dispatching strategy, coarse granularity parallelism between multi GPU cards in GPU Cluster Platform is developed. And inside computing nodes, data buffer technology is used to improve data accessing efficiency and to develop stream parallelism between GPU and CPU. Massive data rapid processing ability of GPU Clsuter System is proved, and its bottleneck is analyzed through geo_rectification experimentes of large zone frame images and line array images.