A Public Domain Tool for Wavelet Image Coding for Remote Sensing and GIS Applications

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1 Proprietary vs. Open Source Models for Software Development A Public Domain Tool for Wavelet Image Coding for Remote Sensing and GIS Applications Project granted by Spanish Government TIC C04-01 and by Catalan Government 2001SGR Joan Serra-Sagristà, UAB - Computer Science Department

2 Goal and Contents To devise a lossy coding system for hyperspectral images for use in remote sensing and geographic information systems. 1. Coding 2. Application 3. Results

3 Introduction 1/4 Motivation for lossy image coding QuickBird 2 sensor has a spatial resolution of 0.82 m 2 panchromatic, 3.28 m 2 multispectral Paris Area of 10km x 10km Resolution: 8 bpp 1 band 10 Mb 10 bands 100 Mb Increasing demand of: storage capability channel bandwidth

4 Introduction 2/4 Some noteworthy figures about satellites nowadays: # satellites in orbit (April 2003): # debris (April 2003): programs of commercial geostationary satellites for satellites for communications Hundreds satellites for non-communications: remote-sensing, meteorological surveys, agricultural data,...

5 Introduction 3/4 Definition. Remote Sensing and Geographic Information Systems RS. Acquisition of information about an object without being in physical contact with it GIS. Ability to create intelligent maps by layering geographic information Use of both aerial and satellite data Applications: Agriculture, Cartography, Environment

6 Introduction 4/4 Particular requirements for RS / GIS applications Zooming Panning High Compression Ratio

7 Image coding 1/3 Lossy Image Coding System: Encoding process Original Image Wavelet Transform

8 A review of Bit Plane Encoding techniques Image coding 2/3 LVQ, 1992 EZW, 1993 IC, 1995 SPIHT, 1996 JPEG2K, 2000 CCSDS, 2004

9 Image coding 3/3 ECW MrSID GeoTIFF JPEG2000 Price Free to to to 6830 Compression ratios 10:1 to 50:1 any Monoband and multiband compression gray and RGB gray and RGB yes Lossy to lossless Lossy Lossy or lossless yes Respect of no-data regions Watermarking yes no yes Zooming and panning yes no yes Random access yes no yes

10 JAVA Application 1/2 Java Advanced Imaging: Render large images Common image operators Web applications Capabilities: 1. Highly scalable compressed data stream 2. Improved compression eficiency 3. Ability to enhance selected spatial regions 4. Ability to work with truly enormous images

11 JAVA Application 2/2 Current state of the application Stage Pre-processing Transform Quantization Bit Plane Encoding Entropy Coding Optimization Implemented Color Conversion, Level Shift, Normalization DWT: Haar, Shore, 5/3, 9/7, D4, Integer WT Scalar quantization, Lattice Vector Quant. EZW, IC, SPIHT, CCSDS Arithmetic coding Bit Allocation Foreseen Local WT, DCT Embedded LVQ, Trellis JPEG2K, SBHP Huffman, Golomb-Rice

12 Results 1/5 Validation of our JAVA implementation L e n a E Z W I C S P I H T

13 Results 2/5 Comparison of Rate Distortion, reviewed methods CASI Image 710x4558x Mb Spatial resol: 3.5 m 2 Rate Distortion Band 06

14 Results 3/5 Comparison of Rate Distortion, reviewed methods Multispectral 7109x Mb Spatial resol: 3.5 m 2 Rate Distortion tr5m

15 Comparison of Visual Quality, reviewed methods Results 4/5

16 Results 5/5 Comparison of Visual Quality, reviewed methods 512x512 Boats image compressed with CCSDS-ILDC and SPIHT at a compression ratio of 40:1.

17 Future Work Which are our plans for the future Reviewed methods Exploit spectral redundancy 3-d DWT 1-d and 3-d SPIHT Embedded LVQ JPEG2K_JPIP: Internet Protocol Standard for Interactive Imaging (Part 9) Proposal of specic multi/hyperspectral image coders Extensions on the CCSDS

18 Conclusions 1. Presentation of a public domain tool for encoding hyperspectral images. These images are used in Remote Sensing and GIS applications. 2. There does not exist today a compression system that takes into account RS and GIS particular requirements. There is a real problem of storing and transmitting images of such huge size. 3. Limited formats in use today are proprietary software. 4. Realize that university, as advanced research centers, supports public domain software for state-of-the-art research projects and teaching activity.

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