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GNR607 Principles of Satellite Image Processing Instructor: Prof. B. Krishna Mohan CSRE, IIT Bombay bkmohan@csre.iitb.ac.in Slot 2 Lecture 2 Introduction to Digital Image Processing July 22, 2014 10.35 AM 11.30 AM IIT Bombay


  1. GNR607 Principles of Satellite Image Processing Instructor: Prof. B. Krishna Mohan CSRE, IIT Bombay bkmohan@csre.iitb.ac.in Slot 2 Lecture 2 Introduction to Digital Image Processing July 22, 2014 10.35 AM – 11.30 AM

  2. IIT Bombay Slide 1 July 22, 2014 Lecture 2 Intro. Image Processing Contents of the Lecture • Concept of a digital image • Digitization • Components of a digital image processing system • Steps in digital image processing GNR607 Lecture 2 B. Krishna Mohan

  3. IIT Bombay Slide 2 What is a Digital Image? A digital image is a representation of the real world, discretized in space with energy reflected / emitted / transmitted by the objects in the image quantized to a finite number of levels GNR607 Lecture 2 B. Krishna Mohan

  4. IIT Bombay Slide 3 Camera Pixel Real World Scene Digital Image GNR607 Lecture 2 B. Krishna Mohan

  5. IIT Bombay Slide 4 Digitization • Digitization involves three steps: – Sampling – Quantization – Coding GNR607 Lecture 2 B. Krishna Mohan

  6. IIT Bombay Slide 5 Sampling • View area divided into cells • Each cell is a picture element pixel • The image now is a matrix of M rows, and N columns • M = Length of View area / Length of Cell • N = Width of View area / Width of Cell • Smaller cell size  better ability to distinguish between closely spaced objects GNR607 Lecture 2 B. Krishna Mohan

  7. IIT Bombay Slide 6 Sampling • In remotely sensed images the sampling is essentially ground sampling – i.e., on the ground a virtual grid is placed and the energy reflected / transmitted / emitted from each grid cell is collected by the sensors and stored as a pixel value • The grid cell corresponds to a pre-defined area on the ground; e.g., 5.8m x 5.8m as in case of ISRO’s Resourcesat Satellite GNR607 Lecture 2 B. Krishna Mohan

  8. IIT Bombay Slide 7 Sampling • Smaller the grid cell area better the details visible in the image • The grid cell corresponding to a pre- defined area on the ground; e.g., 5.8m x 5.8m • This is similar to dpi settings in desktop image scanners. Higher dpi, smaller size of dot, more pixels or cells in the image GNR607 Lecture 2 B. Krishna Mohan

  9. IIT Bombay Slide 7a Spatial Resolution Source not known, to be located! GNR607 Lecture 2 B. Krishna Mohan

  10. IIT Bombay Slide 8 Impact of Pixel Size • Pixel size corresponds to the Instantaneous Field of View (IFOV) of the sensing system • Smaller the IFOV, better is the ability to resolve closely spaced objects (RESOLUTION) • Price to pay – larger size of data • Noise sensitivity of the sensor determines the maximum possible resolution GNR607 Lecture 2 B. Krishna Mohan

  11. IIT Bombay Slide 9 Point to Note • IFOV is 10 metres x 10 metres square does not mean that objects smaller than this size will not be visible • If a smaller object has very high or very low reflectance relative to its background, such object will be visible despite its size being smaller than the pixel’s IFOV GNR607 Lecture 2 B. Krishna Mohan

  12. IIT Bombay Slide 10 Quantization • Reflected / transmitted / emitted energy from the object is converted into an electrical signal • The electrical signal converted to a digital signal by an analog-to-digital converter (ADC). • Digital signal takes a range of values according to the specification of the ADC GNR607 Lecture 2 B. Krishna Mohan

  13. IIT Bombay Slide 11 Shades in Image and Digital Values 0 255 GNR607 Lecture 2 B. Krishna Mohan

  14. IIT Bombay Slide 12 ADC • 8-bit ADC  2 8 distinct values, represented in binary as 00000000 – 11111111, or 0 to 255 in decimal form or 00 to FF in hex • 11-bit ADC  2 11 values, 0 to 2047 • The number of levels indicate the number of distinct individually differentiable levels of received energy GNR607 Lecture 2 B. Krishna Mohan

  15. IIT Bombay Slide 13 Impact of quantization levels 64 levels (6 bit) – more shades visible Source unknown, to be found! 4 levels (2 bit) – severe contouring effect GNR607 Lecture 2 B. Krishna Mohan

  16. IIT Bombay Slide 14 Point to Note • When an image contains regions of fine detail, high spatial resolution (e.g. a stadium with large crowd) is important • When the image contains large regions of very little change such as a close-up of a person, high number of quantization levels is important GNR607 Lecture 2 B. Krishna Mohan

  17. IIT Bombay Slide 15 Pixel Size Less Important Original (After dpi reduced by 50%) GNR607 Lecture 2 B. Krishna Mohan

  18. IIT Bombay Slide 16 Quantization Levels More Important 256 levels 8 levels GNR607 Lecture 2 B. Krishna Mohan

  19. IIT Bombay Slide 17 Pixel Size Important 160 x150 80 x 75 24-bit 24-bit GNR607 Lecture 2 B. Krishna Mohan

  20. IIT Bombay Slide 18 Number of Levels less Important 160 x150 160 x 150 24-bit 8-bit GNR607 Lecture 2 B. Krishna Mohan

  21. IIT Bombay Slide 19 Encoding • Normally the quantized image is binary encoded. • If the number of quantization levels is between 0 and 255, each pixel is represented by 1-byte • If the number of levels exceeds 255, each pixel is assigned two-bytes. • At present, American satellites Quickbird, Ikonos, Indian satellites Cartosat and a few others have 11 bit and 10 bit ADCs and store data in 2 bytes per pixel on disk. GNR607 Lecture 2 B. Krishna Mohan

  22. IIT Bombay Slide 20 Motivation for Digital Image Processing • Why Digital Image Processing for Remote Sensing? – Nature of data (inherently digital) – Flexibility offered by computers – Reducing the bias of human analysts – Standardizing routine operations – Rapid handling of large volumes of data GNR607 Lecture 2 B. Krishna Mohan

  23. IIT Bombay Slide 21 Motivation for Digital Image Processing • Why Digital Image Processing for Remote Sensing? – Certain operations cannot be done manually (removal of distortions) – Generation of different views – Archival in compact/compressed mode – Easy to share and disseminate GNR607 Lecture 2 B. Krishna Mohan

  24. IIT Bombay Slide 22 The Origins of Digital Image Processing Early 1920s: One of the first applications in the news- paper industry, cable transmission between NY and London ––Source: http://www. imageprocessingplace.com GNR607 Lecture 2 B. Krishna Mohan

  25. IIT Bombay Slide 23 Historical Developments • Mid to late 1920s: Improvements to the Bartlane system resulted in higher quality images–New reproduction processes based on photographic techniques–Increased number of tones in reproduced imagesImproved digital image. GNR607 Lecture 2 B. Krishna Mohan

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