GE 113 REMOTE SENSING Topic 7. Image Enhancement Lecturer: Engr. Jojene R. Santillan jrsantillan@carsu.edu.ph Division of Geodetic Engineering College of Engineering and Information Technology Caraga State University
Outline Part 1. Image Enhancement Concepts Part 2. Contrast Manipulation Techniques 2
Expected Outcomes The students would be able to: Learn the concepts behind image enhancement Identify the various computer-assisted procedures of image enhancement Learn how to conduct the computer-assisted procedures through laboratory exercises 3
PART 1. IMAGE ENHANCEMENT CONCEPTS 4
Image Enhancement The goal is to improve the visual interpretability of an image by increasing the apparent distinction between the features in the scene. Why do we need a computer to do the enhancement? Our eyes are poor at discriminating the slight radiometric or spectral differences that may characterize such features With computers, these slight differences can be visually amplified to make them readily observable by our eyes. 5
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Types of Image Enhancement Operations Point Operations Brightness values of each pixel in an image data are modified independently Local Operations Brightness values of each pixel in an image data are modified based on neighboring brightness values Note: Either form of enhancement can be performed on single-band images or on the individual components of multi-image composites. 11
When are image enhancement techniques applied? Normally applied to image data after the appropriate image rectification and restoration procedures have been performed. Noise removal very important to conduct prior to image enhancement Image enhancement techniques may enhance noise if they are not removed the interpreter will end up analyzing enhanced noise! 12
Categories of Image Enhancement Techniques Contrast Manipulation Techniques Discussed in detail in Part 2 Spatial Feature Manipulation Techniques Used to emphasize or deemphasize image data of various spatial frequencies Spatial frequency refers to the roughness of the tonal variations occurring in an image These are local operations pixel values in an original image are modified on the basis of the gray scale/brightness/dn values of neighboring pixels Examples: Spatial filters Multi-image Manipulation Techniques Enhancements involving multiple spectral bands of imagery Examples: Spectral ratioing Principal and canonical components transformation Vegetation components transformation Intensity-hue-saturation color space transformation 13
PART 2. CONTRAST MANIPULATION TECHNIQUES 14
Contrast Manipulation Focused on manipulating the brightness values/dns of an image data to reveal specific or new information or to enhance existing image information Commonly used contrast manipulation procedures: Gray-level thresholding Level slicing Contrast stretching These are all point operations 15
Gray-level Thresholding A segmentation procedure An input image band is segmented into two classes: One class for those pixels having values below a defined gray level (DN) One class for those pixels above this value The result is a binary classification This binary classification can then be applied to a particular image band data to enable display of brightness variations in only a particular class 16
NIR Band of Landsat 7 ETM+ Example: Histogram of DN values of NIR Band DN Range: 0 30 water bodies Gray-scale Thresholded Image: Class 1: 0-30 (Water) Class 2: 31 255 (Others) NIR Band of Landsat 7 ETM+ Showing only Class 1 (Water) True Color Image Showing only Class 1 (Water) 17
Level Slicing An enhancement technique whereby the DNs distributed along the x axis of an image histogram are divided into a series of intervals or slices. All of the DNs falling within a slice are then displayed at a single DN in the output image 18
NIR Band of Landsat 7 ETM+ Example: Histogram of DN values of NIR Band Sliced NIR Band of Landsat 7 ETM+ (6 classes) 19
Example: Sliced NIR Band (Water Portion only) 20
Example: Level slicing the TIR Band of Landsat 7 to show land surface temperature (LST) Image http://www.mdpi.com/2072-4292/7/4/4268/htm 21
Contrast Stretching (1) Recall: An image can have DN values ranging from 0 to a maximum value depending on its radiometric resolution: E.g., an 8-bit image can have DNs ranging from 0 255 A 12-bit image can have DNs ranging from 0 4095 Etc. When the image data are visualized on a screen of a computer, they are displayed as brightness values for each screen pixel A data pixel with a larger value is brighter than one with a smaller value However, unlike the image data, screen pixels can only have 256 unique brightness values (i.e., 0 to 255). This limitation prevents the data from being displayed with brightness exactly equal to their real (DN) value 22
Contrast Stretching (2) Stretching the image data refers to a method by which the data pixels are rescaled from their original values into a range that the monitor can display - namely, into integer values between 0 and 255. But what about contrast stretching? 23
Contrast Stretching (3) The parameters of the stretch can be adjusted to maximize the information content of the display for the features of interest this process is referred to as contrast stretching. Contrast stretching changes contrast in the image Contrast = the relative differences in the brightness of the data values: increasing an image's contrast means the dark pixels will become darker, and the bright pixels will become brighter brightness difference between the two increases 24
Contrast Stretching as an Image Enhancement Procedure Used to expand the narrow range of brightness values typically present in an input image over a wide range of values Contrast stretching results to an output image or image display that is designed to emphasize the contrast between features of interest. 25
Types of Contrast Stretching (as implemented in various image processing software, e.g., Envi) Linear Linear 0-255 Linear 2% Gaussian Equalization Square root ALL OF THESE OPERATIONS RELY ON THE MANIPULATION OF THE IMAGE HISTOGRAMS 26
Number of Students What is a Histogram? a graphical representation of the distribution of numerical data. To construct a histogram, the first step is to "bin" the range of values that is, divide the entire range of values into a series of intervals and then count how many values fall into each interval. The bins are usually specified as consecutive, nonoverlapping intervals of a variable. Exam Score The bins (intervals) must be adjacent, and are usually equal size 27
What is a Image Histogram? A type of histogram that acts as a graphical representation of the tonal ( DN ) distribution in a digital image. It plots the number of pixels for each tonal/dn value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance. 28
Linear Contrast Stretching Sets the image minimum and maximum DN values to values of 0 and 255, and stretches all other data values linearly between 0 to 255. Example: If a band of an image has DN values ranging from 30 to 200, linear contrast stretching will expand the range such that when displayed/outputted to an image file, the new DN values will range from 0 to 255: Screen value of 0 will be assigned to 30 Screen value of 200 will be assigned to 255 All other values will be linearly stretched Algorithm: Where: New DN = DN = [(DN MIN) / (MAX MIN) ] * 255 DN = original DN of a pixel MIN = the image s minimum DN value that will be assigned a new value of 0 MAX = the image s maximum DN value that will be assigned a new value of 255 29
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Example: Linear Contrast Stretching Original Band 1 Stretched 31
Linear 0-255 Sets the image s DN value of 0 to a new value of 0, and the image s DN value of 255 to a new value of 255 No stretching 32
Example: Linear 0-255 Original Band 1 Stretched 33
Linear 2% Sets the highest and lowest 2% of the original image DN values to new values of 0 and 255, and it stretches all other data values linearly 34
Example: Linear 2% Original Band 1 Stretched 35
Gaussian Sets: the original image s mean DN value to a new value of 127, the DN value 3 standard deviations below the mean value to a new value of 0, and the DN value 3 standard deviations above the mean value to a new value of 255. Intermediate values are assigned new value using a Gaussian curve 36
Example: Gaussian Original Band 1 Stretched 37
Histogram Equalization Scales the original image DN values to equalize the number of DNs in each display histogram bin In this approach, image DN values are assigned to the display levels on the basis of their frequency of occurrence 38
Example: Histogram Equalization Original Band 1 Stretched 39
Square root takes the square of the input histogram and applies a linear stretch Original Band 1 Stretched 40
Questions or clarifications? 41
References/Further Reading Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2008). Remote Sensing and Image Interpretation 6th Edition. United States of America: John Wiley & Sons, Inc. Online Tutorial: Fundamentals of Remote Sensing Image Enhancement. Available at http://www.nrcan.gc.ca/earthsciences/geomatics/satellite-imagery-airphotos/satellite-imageryproducts/educational-resources/9389 42