3_1Digital Image.pptx
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This document discusses digital image processing for remote sensing. It begins by defining a digital image as a discretized representation of the real world with light energy quantized to finite levels. It then describes the digitization process which involves sampling, quantization, and encoding. Sampling involves dividing the image area into a grid of cells called pixels. Quantization converts light energy to digital signals using an analog-to-digital converter (ADC) that assigns numeric values. Encoding represents the quantized values in binary. Digital image processing is useful for remote sensing as it allows flexibility, standardization, handling large data volumes, and removing distortions compared to human analysis.
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Recycled Concrete Aggregate in Construction Part II
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5214-1693458878915-Unit 6 2023 to 2024 academic year assignment (AutoRecovere...
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Literature Review Basics and Understanding Reference Management.pptx
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3_1Digital Image.pptx
- 1. DIGITAL IMAGE PROCESSING Dr. Divya Priya School of Civil Engineering SCE, VIT, Vellore
- 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 finite number of levels
- 4. Digitization • Digitization involves three steps: –Sampling –Quantization –Coding
- 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
- 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
- 7. Sampling • Smaller the grid cell area better the details visible in the image • The grid cell corresponding to a predefined 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
- 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
- 9. Point to Note • IFOV is 10 metres x 10 metres square means that objects that are of this size or larger are normally seen as pixels representing these objects • Even if an object is smaller, if it 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
- 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
- 12. ADC • 8-bit ADC 28 distinct values, represented in binary as 00000000 – 11111111, or 0 to 255 in decimal form or 00 to FF in hex • 11-bit ADC 211 values, 0 to 2047 • The number of levels indicate the number of distinct individually differentiable levels of received energy
- 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
- 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.
- 20. 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
- 21. 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
- 28. FCC
- 30. NCC
- 31. Black & White Image • A black/white image is one that has no color but only white, black and shades of gray. • The smallest value at a pixel is 0 (black) • The largest value is 2L-1 (white) • L is the number of bits/pixel • Intermediate values represent shades of gray, from black increasing towards white • For L=8, black = 0, white = 255
- 32. Gray Scale
- 34. Black and White Image