The GIRAF (Robust Geospatial Image Registration and Alignment with Features) project
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The GIRAF (Robust Geospatial Image Registration and Alignment with Features) project

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The GIRAF (Robust Geospatial Image Registration and Alignment with Features) project ...

The GIRAF (Robust Geospatial Image Registration and Alignment with Features) project
Amy Apon, Ph.D., Director of Research
University of Arkansas

GIRAF (Robust Geospatial Image Registration and Alignment with Features) is an NSF-funded project that addresses the problem of massive new volumes of high resolution image data. The system being developed will handle large amounts of data from disparate sources and autonomously process and register new data against existing data. The problem motivates investigation in several interesting computer science and computer engineering areas, including parallel implementation of the algorithms on hybrid platforms, algorithm development for multimode matching, and management of very large scale image repositories.

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The GIRAF (Robust Geospatial Image Registration and Alignment with Features) project Presentation Transcript

  • 1.
  • 2. Geospatial Image Registration and Alignment with Features
    Amy Apon, Ph.D., Director
    Arkansas High Performance Computing Center
  • 3. Project Participants
    An NSF-funded project in collaboration with the Center for Advanced Spatial Technologies (CAST), University of Arkansas
    Jack Cothren, Director, CAST, Assoc. Prof, Geosciences, co-PI
    Wesley Emeneker, Ph.D. student, CSCE
    Seth Warn, Ph.D. student. CSCE
    StanislavBobovych, undergraduate Honor’s student, CSCE
    John Gauch, Professor, CSCE
    Miaoqing Huang, Assistant Professor, CSCE
  • 4. Project Motivation
    GIS research has massive amounts of new data, including high resolution data
    We want to use those pictures to do
    Emergency response
    Plan for urban growth
    Track forestation, manage croplands
    Understand history and create historical records
  • 5.
  • 6.
  • 7. Project Motivation
    Data comes from diverse sources
    Satellite – visible light, near infrared, ultraviolate
    Aerial – airplane takes pictures with a consumer camera
    There are problems with the image data
    Resolution difference – 3 meters per pixel versus 30 centimeters per pixel
    Cloud cover, time of year, angle of shot with aerial data
    In spite of these problems we want to accurately find exactly where on the globe the images are …
  • 8. Goals of GIRAF
    Efficiently process and reference new data against existing data
    Match data from different types of sources (e.g. satellite vs. aerial)
    Image registration – establish a correspondence between two or more images, allowing them to be aligned, overlapped, and transformed into a common coordinate system.
  • 9. Center for Advanced Spatial Technologies University of Arkansas
    From an “unorganized block” …
    19 September 2008
  • 10. Center for Advanced Spatial Technologies University of Arkansas
    Finally, to a georeferenced image mosaic.
    19 September 2008
  • 11. GIRAF Image registration Steps
    Start with a set of images that are accurately georeferenced – call it the reference set
    Preprocess those images to find distinct, unique features
    Feature extraction applications include SIFT, SURF, GLOH, MSER, …
    We use the SIFT program – Scale Invariant Feature Transform [Lowe 1999]. SIFT is invariant to rotation, scale, and translation
    The features should be
    Resistant to changes in rotation and translation, very resistant to scale changes, somewhat resistant to other perspective or projective changes (like skew)
    These steps are done one time for images in the reference set, BUT we still have to deal with issues of scale.
  • 12. GIRAF Image registration Steps
    Extract the significant features in a new image
    With SIFT we expect to get roughly one feature for every 100 pixels in the image
    Find the distance/dissimilarity between the features in the new image and features in image(s) in the reference set.
    Use RANSAC to calculate the most likely candidates for features that match, calculate the homography.
    These steps should be fast!
  • 13. Images as large as 1.2GB require more than 500GB and hours of runtime for feature extraction
    A 1.2GB image runs in 67 seconds on 512 nodes on TeraGrid
    MPI parallelization without ghost rows shows linear speedup and less than 3% loss of features
    Work done by StanislavBobovych, Emeneker, Warn, Cothren, Apon, SC10 Poster
    MPI Parallelization of SIFT Feature Extraction
  • 14. Accelerating Feature-Based Georeferencing on Hybrid Clusters
    A satellite image may have billions of pixels and generate millions of features
    Calculating the dissimilarity of features is computationally expensive
    We use NVIDIA’s C for CUDA on a 6-node cluster consisting of dual Intel quad-core E5520 processors,12GB, two NVIDIA GTX295 graphics cards
    Work done by Seth Warn, Emeneker, Apon, Journal of Parallel Computing Special Issue on Application Acceleration in HPC, submitted
  • 15. SIFT Features
    Feature descriptors are 128-dimensional vectors that are histograms collected from the 16 regions around a feature, values 0 to 255
    Earth Mover’s Distance is better for comparison than Euclidean
    a = ( 0, 0, 0, 0, 0, 0, 0, 10)
    b = ( 0, 0, 0, 0, 0, 0, 5, 5)
    c= ( 0, 0, 0, 5, 0, 0, 0, 5)
    Using Euclidean, distance(a,b) = distance(a,c) = 7.1
    Using EMD, distance(a,b) = 0.625, distance(a,c) = 2.5
  • 16. EMD and Circular Earth Mover’s Distance
    Where A and B are the cumulative histograms of a and b. CEMD is a variation on EMD that takes into account the periodic nature of SIFT descriptor histograms
    Research has shown that CEMD is much better than many other dissimilarity measures for SIFT feature matching
    CEMD is computationally expensive compared to other measures, and not feasible for comparing many large images
  • 17. CUDA Acceleration of CEMD
    Our first implementation achieves a speedup of about 8 over the single CPU version
    Additional speedup requires several optimization steps, paying attention to:
    Use of shared memory
    Memory bank conflicts
    Thread residency
    Loop unrolling
    Algorithm optimization
    Our final implementation is 75x faster
    than the optimized single CPU version.
    Time to match 40K x 40K features was
    reduced from 1.5 hours to 5 minutes.
  • 18. Streaming Kernel Execution
    If the sum total of memory required by the input arrays of descriptors and the output array of results is larger than device memory, result calculation must be broken into multiple kernel launches.
    GPU devices with compute capability of 1.1 or higher can overlap the execution of a kernel with a single device-host memory operation
    With 24 GPUs in six nodes, the test cluster performed over 1.2 billion comparisons per second
    The utility of optimizations will vary widely between different kernels. With CEMD coalescing global memory accesses had little effect, but simple loop unrolling was one of the most useful refinements
  • 19. Robust Processing of Data from Different Sources
    One simple technique uses histogram classification to determine how well two images will match
    Histogram equalization can improve matching between images
  • 20. Registration on Hybrid Clusters
    We are developing a framework and cluster platform to stream large sets of new images for matching with images in a reference library.
    Take new image data that we don't trust to be correct and...
    accurately and exactly georeference it
    quickly
    without human intervention
    and make it easy for people to use
  • 21. Discussion and Questions
    Amy Apon, Ph.D.
    Professor, Computer Science and Computer Engineering
    University of Arkansas
    aapon@uark.edu