NASA Conference Publication 3315 1995 Science Information Management and Data Compression Workshop Edited by James C. Tilton Goddard Space Flight Center Greenbelt, Maryland Proceedings of a workshop sponsored by the National Aeronautics and Space Administration in cooperation with the WashingtonNorthern Virginia Chapter of the Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society and held at NASA Goddard Space Flight Center Greenbelt, Maryland October 26 - 27, 1995 National Aeronautics and Space Administration Goddard Space Flight Center Greenbelt, Maryland 1995 800 Elkridge Landing Road, Linthicum Heights, MD 21090-2934, (301) 621-0390. I This publication is available from the NASA Center for AeroSpace Information, FOREWORD The Science Information Management and Data Compression Workshop was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. This NASA Conference Publication serves as the proceedings for the workshop. The workshop organized by the Information Sciences Technology Branch, Space Data and Computing Division of the NASA Goddard Space Flight Center, and was supported by the Office of Mission to Planet Earth, NASA Headquarters. The workshop was held in cooperation with the Washington/Northern Virginia Chapter of the Institute of Electrical and Electronics Engineers (IEEE) Geoscience and Remote Sensing Society. The goal of the Science Information Management and Data Compression Workshop was to explore promising computational approaches for handling the collection, ingestion, archival and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. Papers were selected from papers submitted in response to a widely distributed Call for Papers. Fourteen papers were presented in 3 sessions. Discussion was encouraged by scheduling ample time for each paper. The workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center. Workshop Organizers James C. Tilton Robert F. Cromp Mail Code 935 Mail Code 935 NASA GSFC NASA GSFC Greenbelt, MD 20771 Greenblet, MD 20771 phone: (301) 286-9510 phone: (301) 286-4351 FAX: (301) 286-1776 FAX: (301) 286-1776 Internet: Internet: tilton @chrpisis.gsfc.nasa.gov cromp @ sauquoit, gsfc.nasa.gov iii CONTENTS A Spatially Adaptive Spectral Re-ordering Technique for Lossless Coding of Hyper-spectral Images .............................. 1 -/ Nasir D. Memon, Northern Illinois University, DeKalb, IL, USA; and Nikolas Galatsanos, Illinois Institute of Technology, Chicago, IL, USA A Comparison of Model-Based VQ Compression with other VQ Approaches ........... 13 -2--- Mareboyana Manohar, Bowie State University, Bowie, MD, USA; and James C. Tilton, NASA Goddard Space Flight Center, Greenbelt, MD, USA Remotely Sensed Image Compression Based on Wavelet Transform ................ 23 _ 3 Seong W. Kim, Heung K. Lee, Soon D. ChoL Korea Advanced Institute of Science and Technology, Taejon, Korea; and Kyung S. Kim, Korea Institute of Science and Technology, Taejon, Korea A Packet Data Compressor ......................................... 35 - Z/ Mitchell R. Grunes, Allied Signal Technical Services Corp.; and Junho Choi, Naval Research Laboratory, Washington, DC, USA Reduction and Coding of Synthetic Aperture Radar Data with Fourier Transforms ........ 45 - _5"- David G. TiIley, Elkridge, MD, USA Selecting a General-Purpose Data Compression Algorithm ...................... 55 --_' Gary Jason Mathews, NASA Goddard Space Flight Center, Greenbelt, MD, USA Compression Research on the REINAS Project ............................. 65 -7 Glen Langdon, Jr., Alex Pang, Craig M. Wittenbrink, Eric Rosen, William Macy, Bruce R. Montague, Caries Pi-Sunyer, Jim Spring, David Kulp, Dean Long, Bryan Mealy and Patrick Mantey, Baskin Center for Computer Engineering & Information Sciences, University of California-Santa Cruz, Santa Cruz, CA, USA KRESKA: A Compression System for Small and Very Large Images ............... 75 Krystyna W. Ohnesorge and Rene Sennhauser, MultiMedia Laboratory, University of Ziirich, Ziirich, Switzerland Alternate Physical Formats for Storing Data in HDF .......................... 91 - Mike Folk and Quincey Koziol, National Center for Supercomputing Applications, University of Illinois, Champaign-Urbana, IL, USA Context-Based Retrieval of Remote Sensed Images Using a Feature-Based Approach ..... 103 - / 0 Asha Vellaikal and Son Dao, Information Sciences Lab, Hughes Research Laboratories, Malibu, CA, USA; and C.-C. Jay Kuo, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA Machine Learning for a Toolkit for Image Mining .......................... 115 - / / Richard L. Delanoy, Massachusetts Institute of Technology / Lincoln Laboratory, Lexington, MA, USA V Dissemination of Compressed Satellite Imagery within the .. 123 ./L Navy SPAWAR Central Site Product Display Environment .................. Oleg KiseIyov and Paul Fisher, Computer and Information Sciences, Inc., Denton, TX, USA Data Management and Scientific Integration within the Atmospheric Radiation Measurement Program ............................ 131 " 1_'_ Deborah K. Gracio, Larry D. Ha_ield, Kenneth R. Yates and Jimmy W. Voyles, Pacific Northwest Laboratory, Richland, WA, USA; Joyce L. Tichler, Brookhaven National Laboratory, Upton, NY, USA; Richard T. Cederwall, Mark J. Laufersweiler, Martion J. Leach, Lawrence Livermore National Laboratory, Livermore CA, USA; and Paul Singley, Oak Ridge National Laboratory, Oak Ridge, TN, USA Object-Oriented Structures Supporting Remote Sensing Databases ................ 139" i Keith Wichmann, Global Science and Technology, Greenbelt, MD, USA; and Robert F. Cromp, NASA Goddard Space Flight Center, Greenbelt, MD, USA vi N96-15447 ),/ / / / A Spatially Adaptive Spectral Re-Ordering Technique for Lossless Coding of Hyper-spectral Images Nasir D. Memon Nikolas Galatsanos Department of Computer Science, Electrical Engineering Northern Illinois University, Illinois Institute of Technology Dekalb, IL 60115 Chicago, IL [email protected] [email protected] Abstract In this paper, we propose a new approach, applicable to lossless compression of hyper-spectral images, that alleviates some limitations of linear prediction as applied to this problem. According to this approach, an adaptive re-ordering of the spec- tral components of each pixel is performed prior to prediction and encoding. This re-ordering adaptively exploits, on a pixel-by-pixel basis, the presence of inter-band correlations for prediction. Furthermore, the proposed approach takes advantage of spatial correlations, and does not introduce any coding overhead to transmit the order of the spectral bands. This is accomplished by using the assumption that two spatially adjacent pixels are expected to have similar spectral relationships. We thus have a sim- ple technique to exploit spectral and spatial correlations in hyper-spectral data sets, leading to compression performance improvements as compared to our previously re- ported techniques for lossless compression. We also look at some simple error modeling techniques for further exploiting any structure that remains in the prediction residuals prior to entropy coding. 1 Introduction Recent years have seen a tremendous increase in the generation, transmission and storage of multi-spectral images. With the advent of high spectral resolution images, also known as hyper-spectral images, the need for efficient compression algorithms has become increasingly important. This is due to the high data rates resulting from the large number of spectral bands in such images. For example the High-Resolution Imaging Spectrometer (HIRIS) to be placed on the Earth Observing System (EOS) scheduled to be launched by NASA within this decade, is designed to acquire images with 192 spectral bands at 30m spatial resolution with resulting bit-rates of more than 200 Megabits per second. Although much work has been done towards developing algorithms for compressing image data, sophisticated techniques that exploit the special nature of multi-spectral images have started emergingonly recently[6, 13]. More specifically,as noticed by researchersin other imageprocessingareas, the spectral correlation in such imagescannot be assumedto be stationary in nature. In other words, the correlation between bands i and j cannot be assumed the same as that of bands i+ k and j + k (see for example [4]). Therefore, techniques based on a stationarity assumption, that have been used for capturing spatial correlations in 2-D images, will yield sub-optimal results when directly extended to the third dimension and applied to hyper-spectral data sets. Very recently, techniques were proposed to capture the non-stationary nature of the spectral correlation of hyper-spectral images for lossy image compression [13, 12, 5]. However, because of the difference in goals, the optimal way of exploiting spatial and spectral correlations for lossy and lossless compression is different. Furthermore, many powerful tools used in lossy compression, such as transform coding and vector quantization, are not applicable to lossless compression. The two most popular approaches for lossless coding of spatially correlated images