Image enhancement. Introduction to Photogrammetry and Remote Sensing (SGHG 1473) Dr. Muhammad Zulkarnain Abdul Rahman

Transcription

1 Image enhancement Introduction to Photogrammetry and Remote Sensing (SGHG 1473) Dr. Muhammad Zulkarnain Abdul Rahman

2 Image enhancement Enhancements are used to make it easier for visual interpretation and understanding of imagery Subtle differences in brightness value can be highlighted either by: Contrast modification or by assigning quite different colours to those levels (density slicing) Point operations change the value of each individual pixel independent of all other pixels Local operations change the value of individual pixels in the context of the values of neighboring pixels

3 Image enhancement Information enhancement includes: Image reduction, Image magnification, Transect extraction, Contrast adjustments (linear and non-linear), Band rationing, Spatial filtering, Fourier transformations, Principle components analysis, Image sharpening, and Texture transformations

4 Visualization Color spaces for visualization -Three approaches: Red-Green-Blue (RGB) space based on additive principle of colors The way TV and computer screen operate 3 channel (R,G,B) Intensity-Hue-Saturation (IHS) space Yellow-Magenta-Cyan (YMC) space -based on subtractive principle of colors

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6 Contrast enhancement Materials or objects reflect or emit similar amounts of radiant flux (so similar pixel value) Only intended to improve the visual quality of a displayed image by increasing the range (spreading or stretching) of data values to occupy the available image display range (usually 0-255) Linear technique Minimum-maximum contrast stretch Percentage linear contrast stretch Standard devia on contrast stretch Piecewise linear contrast stretch Non-linear technique Histogram equaliza on

7 Minimum-maximum contrast stretch

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9 Original Contrast Stretching of Predawn Thermal Infrared Data of the the Savannah River Minimummaximum +1 standard deviation Jensen, 2011

10 Piecewise linear contrast stretch Characterised by a set of user specified break points

11 Histogram equalization In practice a perfectly uniform histogram cannot be achieved for digital image data To make sure that each bar in the image histogramhas the same height Such a histogram has associated with it an image that utilises the available brightness levels equally and Should give a display in which there is good representation of detail at all brightness values The method of producing a uniform histogram is known generally as histogram equalization Reduces the contrast in the very light or dark parts of the image associated with the tails of a normally distributed histogram

12 Contrast Stretching of Predawn Thermal Infrared Data of the the Savannah River Specific percentage linear contrast stretch designed to highlight the thermal plume Histogram Equalization Jensen, 2011

13 Band ratioing BV i, j, ratio = BV BV i, j, k i, j, l where: BV i,j,k is the original input brightness value in band k BV i,j,l is the original input brightness value in band l BV i,j,ratio is the ratio output brightness value

14 Band Ratioing of Charleston, SC Landsat Thematic Mapper Data

15 Band Ratio Image Landsat TM Band 4 / Band 3

16 Spatial filtering Spatial Filtering to Enhance Low-and High-Frequency Detail and Edges A characteristics of remotely sensed images is a parameter called spatial frequency, defined as the number of changes in brightness value per unit distance for any particular part of an image Spatial frequencyin remotely sensed imagery may be enhanced or subdued using two different approaches: Spatial convolution filteringbased primarily on the use of convolution masks, and Fourier analysiswhich mathematically separates an image into its spatial frequency components

17 Spatial Convolution Filtering A linear spatial filteris a filter for which the brightness value (BV i,j,out ) at location i,jin the output image is a function of some weighted average (linear combination) of brightness values located in a particular spatial pattern around the i,jlocation in the input image The process of evaluating the weighted neighboring pixel values is called two-dimensional convolution filtering.

18 Spatial Convolution Filtering The size of the neighborhood convolution mask or kernel (n) is usually 3 x 3, 5 x 5, 7 x 7, 9 x 9, etc. We will constrain our discussion to 3 x 3 convolution masks with ninecoefficients, c i, defined at the following locations: c 1 c 2 c 3 Mask template= c 4 c 5 c 6 c 7 c 8 c

19 Spatial Convolution Filtering The coefficients, c 1, in the mask are multiplied by the following individual brightness values (BV i ) in the input image: c1x BV1 c2x BV2 c3x BV3 Mask template = c4x BV4 c5x BV5 c6x BV6 c7x BV7 c8x BV8 c9x BV9 The primary input pixel under investigation at any one time is BV 5 = BV i,j

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21 Spatial Convolution Filtering: Low Frequency Filter LFF 5, out = int 9 i= 1 BV + BV + BV +... BV = int c i 9 BV n i

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