CPU/GPU near real-time preprocessing for ZY-3 satellite images: Relative radiometric correction, MTF compensation, and geocorrection

Abstract ZY-3 is the first high-accuracy civil stereo-mapping optical satellite of China. It greatly improves China’s optical satellite image resolution with a boom in data volume, calling for new challenges in processing real-time applications. On the other hand, using central processing unit (CPU)/graphic processing unit (GPU) to resolve data-intensive remote sensing problems becomes a hot issue. In this paper, we present an approach for CPU/GPU near real-time preprocessing of ZY-3 satellite images, focusing on three key processors: relative radiometric correction (RRC), modulation transfer function compensation (MTFC), and geocorrection (GC). First, basic GPU implementation issues are addressed to make the processors capable of processing with GPU. Second, three effective GPU specific optimizations are applied for further improvement of the GPU performance. Furthermore, to fully exploit the CPU’s computing horsepower within the system, a CPU/GPU workload distribution scheme is proposed, in which CPU undertakes partial computation to share the workloads of GPU. The experimental result shows that our approach achieved an overall 48.84-fold speedup ratio in ZY-3 nadir image preprocessing (the corresponding run time is 11.60 s for one image), which is capable of meeting the requirement of near real-time response to the applications that follow. In addition, with the supportability of IEEE 754–2008 floating-point standard in the Fermi type GPU, preprocessing ZY-3 images with our CPU/GPU processors could maintain the quality of image preprocess as done traditionally with CPU processors.

[1]  Barbara Chapman,et al.  Using OpenMP - portable shared memory parallel programming , 2007, Scientific and engineering computation.

[2]  Bormin Huang,et al.  GPU Acceleration of Tsunami Propagation Model , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Tom R. Halfhill NVIDIA's Next-Generation CUDA Compute and Graphics Architecture, Code-Named Fermi, Adds Muscle for Parallel Processing , 2009 .

[4]  Jianya Gong,et al.  GPU-accelerated MRF segmentation algorithm for SAR images , 2012, Comput. Geosci..

[5]  Xiaoqian Zhu,et al.  CPU/GPU computing for long-wave radiation physics on large GPU clusters , 2012, Comput. Geosci..

[6]  B. Forster,et al.  Estimation of SPOT P-mode point spread function and derivation of a deconvolution filter , 1994 .

[7]  Julien Michel,et al.  Remote Sensing Processing: From Multicore to GPU , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  R. Richter,et al.  Sensor: a tool for the simulation of hyperspectral remote sensing systems , 2001 .

[9]  Alex Fit-Florea,et al.  Precision and Performance: Floating Point and IEEE 754 Compliance for NVIDIA GPUs , 2011 .

[10]  María Calvino-Cancela,et al.  GPU Geocorrection for Airborne Pushbroom Imagers , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Akila Gothandaraman,et al.  Comparing Hardware Accelerators in Scientific Applications: A Case Study , 2011, IEEE Transactions on Parallel and Distributed Systems.

[12]  Chein-I Chang,et al.  High Performance Computing in Remote Sensing , 2007, HiPC 2007.

[13]  Mi Wang,et al.  Research on GPU-Based Real-Time MTF Compensation Algorithm , 2011, 2011 International Symposium on Image and Data Fusion.

[14]  Thomas Hobiger,et al.  Computation of Troposphere Slant Delays on a GPU , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Pejman Tahmasebi,et al.  Accelerating geostatistical simulations using graphics processing units (GPU) , 2012, Comput. Geosci..

[16]  Tobias Storch,et al.  Processors for ALOS Optical Data: Deconvolution, DEM Generation, Orthorectification, and Atmospheric Correction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Antonio J. Plaza,et al.  Recent Developments in High Performance Computing for Remote Sensing: A Review , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Uwe Stilla,et al.  Hybrid GPU-Based Single- and Double-Bounce SAR Simulation , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Jingyu Yang,et al.  Research on Orthorectification of Remote Sensing Images Using GPU-CPU Cooperative Processing , 2011, 2011 International Symposium on Image and Data Fusion.

[20]  Michael Griebel,et al.  Solving incompressible two-phase flows on multi-GPU clusters , 2013 .

[21]  E. Mikhail,et al.  Introduction to modern photogrammetry , 2001 .

[22]  Firas Hamze,et al.  A Performance Comparison of CUDA and OpenCL , 2010, ArXiv.

[23]  Doo Chun Seo,et al.  Image restoration of the asymmetric point spread function of a high-resolution remote sensing satellite with time-delayed integration , 2011 .

[24]  Qian Du,et al.  High Performance Computing for Hyperspectral Remote Sensing , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Li Kuang,et al.  Accelerating geospatial analysis on GPUs using CUDA , 2011, Journal of Zhejiang University SCIENCE C.

[26]  Bormin Huang,et al.  GPU Acceleration of the Updated Goddard Shortwave Radiation Scheme in the Weather Research and Forecasting (WRF) Model , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Gang Li,et al.  Doppler Keystone Transform: An Approach Suitable for Parallel Implementation of SAR Moving Target Imaging , 2008, IEEE Geoscience and Remote Sensing Letters.