國家實驗研究院國家太空中心的福爾摩沙衛星五號為國內首顆自製的遙測人造衛星，它的用途是自繞地軌道上以光學照相機拍照，提供高解析度的地表影像。此衛星提供全色二公尺及彩色四公尺解析度，二十四公里影像寬度，影像資料產生速度高達970Mbps，每分鐘影像資料量未壓縮前高達7273 Mbytes，對於遙測照相儀的處理速度和儲存空間都是嚴苛的考驗。
本論文是以福衛五號適用的影像壓縮功能為研究對象目標，以硬體處理方式進行高速和即時的衛星影像壓縮，以符合資料傳送頻寬、資料儲存空間和低耗電的限制，並且保持良好的影像品質。壓縮方法採用CCSDS 122.0-B-1[1]所訂規範，使用Discrete Wavelet Transfer (DWT) 和 Bit Plane Encoder (BPE)方法。無失真壓縮比為1.5，失真壓縮比為3.75和7.5。壓縮硬體使用Xilinx Virtex 5 太空等級的XQR5VFX130 FPGA晶片，搭配外部記憶體來達成。使得遙測照相儀能容許全色和彩色同時照相並即時傳出，記憶體空間能儲存拍照16.3分鐘的影像資料。不僅壓縮過的影像品質，符合CCSDS數據，耗電/壓縮速度比也低，僅0.06 Watt/Msamples/sec。所以，本論文所提的影像壓縮設計，符合福爾摩沙五號衛星的任務需求。並且設計架構具彈性，可擴充到未來更多衛星資料壓縮應用領域。

The FORMOSAT-5 is the first remote sensing satellite program that the National Space Organization (NSPO) of the National Applied Research Laboratories (NARL) takes full responsibility for the complete satellite system engineering design. It is an optical remote sensing satellite which can provide remote sensing images with 2m resolution for panchromatic (PAN) image, 4m resolution for multi-spectral (MS) image, and 24km swath width from 720-km altitude earth orbit. The image data generation rate is high to 970Mbps before compression which is equivalent to 7273Mbytes per minute. This proposes critical challenge for the Remote Sensing Instrument (RSI) design on the data processing speed and data storage space.
This thesis is to provide a hardware solution for FORMOSAT-5 satellite to achieve high speed and near real time throughput on image data compression. The data transmission bandwidth, data storage size, and low power consumption constraints from FORMOSAT-5 can be met and good image quality can still be remained. The image data compression method complies with the Consultative Committee for Space Data Systems (CCSDS) standard 122.0-B-1[1] with Discrete Wavelet Transfer (DWT) and Bit Plane Encoder (BPE) methodolgy. The compression ratio is 1.5 for lossless compression, 3.75 or 7.5 for lossy compression. The space grade Xilinx Virtex-5Q FPGA (Field Programmable Gate Arrays), XQR5VFX130, with external memory is used to achieve near real time compression. The design in the thesis can make Remote Sensing Instrument to take PAN and MS image simultaneously and output image data at near real time. The data volume in the RSI allows storing 16.3 minutes imaging data. The image quality after compression and decompression process can match the quality level shown in CCSDS standards 122.0-B-1. The power consumption is only 0.06 Watt/Msamples/sec. So, the design described in this thesis can meet FORMOSAT-5 needs. Furthermore, the design architecture is flexible and extendable that can be used in more satellite data compression application in future.

[1]CCSDS, “Image Data Compression. Recommendation for Space Data System Standards”, CCSDS 122.0-B-1. Blue Book. Issue 1. Washington, D.C., USA: CCSDS, November 2005.
[2]CCSDS, “Image Data Compression. Report Concerning Space Data Systems Standards”, CCSDS 120.1-G-1. Green Book. Issue 1. Washington, D.C., USA: CCSDS, June 2007.
[3]Joint Photographic Experts Group (JPEG) standard, http://www.jpeg.org/jpeg/index.html
[4]Ram Shankar Pathak, “The wavelet transform”, Atlantis Press, ISSN:1875-7642 ; Paris, 2009
[5]J.M. Shapiro, “Embedded image coding using zero trees of wavelet coefficients”, IEEE Trans. on Signal Processing 41(12): 3445–3462, 1993
[6]A. Said, W.A. Pearlman, “A new, fast, and efficient image codec based on set partitioning in hierarchical trees”, IEEE Trans. on Circuits and Systems for Video Technology 6(3): 243–250, 1996
[7]D. Taubman, “High-performance scalable image compression with EBCOT”, IEEE Trans. on Image Processing 9(7): 1158–1170, 2000
[8]Information Technology—JPEG2000 Image Coding System—Part 1: Core Coding System. ISO/IEC 15444–1, 2000.
[9]CCSDS, “CCSDS 122.0 released 12-bits images”, http://cwe.ccsds.org/sls/docs/sls-dc, (2007)
[10]A. Kiely, “Selecting the Golomb Parameter in Rice Coding.” The Interplanetary Network Progress Report 42, no. 159 (November 15, 2004).
[11]M.J. Weinberger, G. Seroussi, and G. Sapiro. “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS.” IEEE Transactions on Image Processing 9, no. 8 (August 2000): 1309-1324.
[12]NSPO Web page about FORMOSAT-5 mission, http://www.nspo.org.tw/2008e/projects/project5/intro.htm
[13]C.F. Chang, Albert Lin, “FORMOSAT-5 Remote Sensing Instrument Electronic Unit Requirements Document”, NSPO, 2012
[14]Albert Lin, et al. “Implementation of CCSDS data compression for remote sensing image”, SPIE Vol. 7810 78100W-2, 2010
[15]C.F. Chang, "FORMOSAT-5 Remote Sensing Telemetry Design Description For Ground Image Source Processing", NSPO, 2012
[16]Albert Lin, et al. “Field-programmable gate array implementation of Consultative Committee for Space Data Systems image data compression”, Journal of Applied Remote Sensing, Vol. 6, 2012 (Accepted)
[17]Albert Lin, “Hardware Implementation of a Real-Time Image Data Compression for Satellite Remote Sensing”, Remote Sensing – Advanced Techniques and Platforms, InTech, 2012.
[18]Xilinx, Xilinx Virtex-5 FPGA User Guide, UG190 (v5.4) March 16, 2012
[19]CMOS Sensor Inc., Workshop on Utilizing the CMOS Sensor for Space Applications, NSPO, 2009
[20]R.F. Rice, “Some Practical Universal Noiseless Coding Techniques”, JPL-PUB-79-22; NASACR-158515. Pasadena, California: JPL, 1979.
[21]Paul Winterrowd, et al. “A 320 Mbps Flexible Image Data Compressor for Space Applications”, IEEEAC paper#1311, 2009
[22]Analog Device Inc., “Wavescale Video Codec ADV212” data sheet, 2008
[23]Hongqiang Wang, “CCSDS Image Data Compression C source codes”, http://hyperspectral.unl.edu/, University of Nebraska-Lincoln, Sept 2008