GE 110 Remote Sensing Lecture 7 PDF

GE 110: REMOTE SENSING Lecture 7: Image Enhancement ENGR. ARTURO G. CAUBA JR. Instructor I College of Engineering and Geosciences Caraga State University GE 110: Remote Sensing Lecture 7 Outline ▪ Image Enhancement Concepts ▪ Contrast Manipulation Techniques GE 110: R...

GE 110: REMOTE SENSING Lecture 7: Image Enhancement ENGR. ARTURO G. CAUBA JR. Instructor I College of Engineering and Geosciences Caraga State University GE 110: Remote Sensing Lecture 7 Outline ▪ Image Enhancement Concepts ▪ Contrast Manipulation Techniques GE 110: Remote Sensing Lecture 7 Expected Outcomes The students would be able to: Learn the concepts behind image enhancement; Identify the various computer-assisted procedures of image enhancement; and Learn how to conduct the computer-assisted procedures through laboratory exercises. GE 110: Remote Sensing Lecture 7 IMAGE ENHANCEMENT CONCEPTS GE 110: Remote Sensing Lecture 7 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. GE 110: Remote Sensing Lecture 7 GE 110: Remote Sensing Lecture 7 7 GE 110: Remote Sensing Lecture 7 GE 110: Remote Sensing Lecture 7 GE 110: Remote Sensing Lecture 7 GE 110: Remote Sensing Lecture 7 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. GE 110: Remote Sensing Lecture 7 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! GE 110: Remote Sensing Lecture 7 Categories of Image Enhancement Techniques (1) 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 based on the gray scale/brightness/DN values of neighboring pixels – Examples: Spatial filters GE 110: Remote Sensing Lecture 7 Categories of Image Enhancement Techniques (2) 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 GE 110: Remote Sensing Lecture 7 CONTRAST MANIPULATION TECHNIQUES  GE 110: Remote Sensing Lecture 7 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 16 GE 110: Remote Sensing Lecture 7 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 GE 110: Remote Sensing Example: Lecture 7 NIR Band of Landsat 7 ETM+ Histogram of DN values of NIR Band DN Range: 0 – 30 → water bodies Gray-scale Thresholded Image: Class 1: 0 -30 (Water) NIR Band of Landsat 7 ETM+ True Color Image Class 2: 31 – 255 (Others) Showing only Class 1 (Water) Showing only Class 1 (Water) Lecture N TOPIC GE 113: Remote Sensing GE ENHANCEMENT 17 GE 110: Remote Sensing Lecture 7 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 the DNs falling within a ‘slice’ are then displayed at a single DN in the output image GE 110: Remote Sensing Example: Lecture 7 NIR Band of Landsat 7 ETM+ Histogram of DN values of NIR Band “Sliced” NIR Band of Landsat 7 ETM+ (6 classes) 20 GE 110: Remote Sensing Lecture 7 Example: Sliced NIR Band (Water Portion only)  GE 110: Remote Sensing Lecture 7 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 GE 110: Remote Sensing Lecture 7 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  GE 110: Remote Sensing Lecture 7 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? GE 110: Remote Sensing Lecture 7 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  GE 110: Remote Sensing Lecture 7 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.  GE 110: Remote Sensing Lecture 7 Types of Contrast Stretching (as implemented in various image processing software, e.g., Envi) Linear Linear 0-255 ALL OF THESE OPERATIONS Linear 2% RELY ON THE MANIPULATION Gaussian OF THE IMAGE HISTOGRAMS Equalization Square root  GE 110: Remote Sensing Lecture 7 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 Number of Students range of values into a series of intervals—and then count how Exam Score many values fall into each interval. The bins are usually specified as consecutive, non- overlapping intervals of a variable. The bins (intervals) must be adjacent, and are usually equal size GE 110: Remote Sensing Lecture 7 What is an 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. GE 110: Remote Sensing Lecture 7 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: New DN = DN’ = [(DN – MIN) / (MAX – MIN) ] * 255 Where: 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 GE 110: Remote Sensing Lecture 7 GE 110: Remote Sensing Lecture 7 Example: Linear Contrast Stretching Original Band 1 Stretched GE 110: Remote Sensing Lecture 7 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” GE 110: Remote Sensing Lecture 7 Example: Linear 0-255 Original Band 1 Stretched GE 110: Remote Sensing Lecture 7 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 GE 110: Remote Sensing Lecture 7 Example: Linear 2% Original Band 1 Stretched  GE 110: Remote Sensing Lecture 7 Gaussian Sets: – the original image’s mean DN value to a new value of 127, – the DN value of 3 standard deviations below the mean value to a new value of 0, and – the DN value of 3 standard deviations above the mean value to a new value of 255. Intermediate values are assigned new value using a Gaussian curve  GE 110: Remote Sensing Lecture 7 Example: Gaussian Original Band 1 Stretched  GE 110: Remote Sensing Lecture 7 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 GE 110: Remote Sensing Lecture 7 Example: Histogram Equalization Original Band 1 Stretched GE 110: Remote Sensing Lecture 7 Square root takes the square of the input histogram and applies a linear stretch Original Band 1 Stretched GE 110: Remote Sensing Lecture 7 Questions or clarifications? GE 110: Remote Sensing Lecture 7 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/earth- sciences/geomatics/satellite-imagery-air- photos/satellite-imagery- products/educational-resources/9389 GE 110: Remote Sensing Lecture 7 Thank you for listening! ☺☺☺