Preparing Remote Sensing Data for Natural Resources Mapping (image enhancement, rectifications ) Why is this important What are the major approaches Examples of digital image enhancement Follow up exercises Image Enhancement Image enhancement is the process of making an image more interpretable for a particular application. Enhancement makes important features of raw remotely sensed data more interpretable to human eyes. Enhancement techniques are often used instead of classification techniques for feature extraction - studying and locating areas and objects on the ground and deriving useful information from images. 1
Image enhancement techniques in ERDAS IMAGINE include: 1. Data correction - radiometric and geometric correction 2. Radiometric enhancement - enhancing images based on the values of individual pixels 3. Spatial enhancement - enhancing images based on the values of individual and neighboring pixels 4. Spectral enhancement - enhancing images by transforming the values of each pixel on a multi-band basis Image enhancement may be performed temporarily when an image is displayed, or permanently on the image data in the data file. An example of simple image Reduction (zooming out): 2
Image enhancement may be performed temporarily when an image is displayed, or permanently on the image data in the data file. An example of image magnification (Zooming in) 3
Radiometric Enhancement Radiometric enhancement deals with individual values of pixels in the image. Radiometric enhancement of a multi-band image can be considered as a series of independent, single-band enhancement. Radiometric enhancement usually does not bring out the contrast of every pixel in an image. Contrast can be lost between some pixels, while gained on others. 4
Image Enhancement: Example: Displaying an image without applying stretch Visually Enhanced Image Original Image Linear Contrast Stretch A linear contrast stretch is a simple way to improve the visible contrast of an image. It is often necessary to contrast-stretch raw image data, so that darker pixels can be seen on the display. 5
Linear Contrast Stretch In most raw data, data file values fall within a narrow range usually a range much narrower than the display device is capable of displaying. That range can be expanded to utilize the total range of the display device (usually 0 to 255). Linear Contrast Stretch Contrast enhancement (or contrast stretching) expands the original input brightness values to make use of the total range or sensitivity of the input device (e.g., output level of 0-255). Linear contrast enhancement is best applied to remotely sensed images with Gaussian or near-gaussian histograms. 6
Min.-Max. Contrast Stretch +1 Standard Deviation Contrast Stretch Linear Contrast Enhancement: Minimum- Maximum Contrast Stretch where: -BV in is the original input brightness value - quant k is the range of the brightness values that can be displayed on the CRT (e.g., 255), min k is the minimum value in the image, max k is the maximum value in the image, and BV out is the output brightness value 7
Linear Contrast Enhancement: Minimum- Maximum Contrast Stretch All other original brightness values between 5 and 104 are linearly distributed between 0 and 255. Original Min.-Max. Contrast Stretch +1 Standard Deviation Contrast Stretch Minimum-maximum +1 standard deviation 8
Piecewise Linear Contrast Stretch A piecewise linear contrast stretch allows for the enhancement of a specific portion of data by dividing the lookup table into three sections: low, middle, and high. Or, the selective pieces of the histogram are linearly contrast stretched. It enables the user to create a number of straight line segments which can simulate a curve. The user can enhance the contrast or brightness of any section in a single color at a time. This technique is very useful for enhancing image areas in shadow or other areas of low contrast. A lookup table is an ordered set of numbers, which is used to perform a function on a set of input values. To display or print an image, lookup tables translate file values into brightness values. Input Output 30 > 0 31 > 25 32 > 51 33 > 76 34 > 102 35 > 127 36 > 153 37 > 178 38 > 204 39 > 229 40 > 255 9
Nonlinear Contrast Stretch A nonlinear spectral enhancement can be used to gradually increase or decrease contrast over a range, instead of applying the same amount of contrast (slope) across the entire image. Usually, nonlinear enhancements bring out the contrast in one range while decreasing the contrast in other ranges. 10
Histogram Matching Histogram matching is the process of determining a lookup table that will convert the histogram of one image to resemble the histogram of another. Landsat TM (August 1995 and 1998, Bands 4,3,2) Histogram matching is useful for matching data of the same or adjacent scenes that were scanned on separate days, or are slightly different because of sun angle or atmospheric effects. This is especially useful for mosaicking or change detection. 11
Histogram Matching To achieve good results with histogram matching, the two input images should have similar characteristics: 1. The general shape of the histogram curves should be similar. 2. Relative dark and light features in the image should be the same. 3. For some applications, the spatial resolution of the data should be the same. 4. The relative distributions of land covers should be about the same, even when matching scenes that are not of the same area. If one image has clouds and the other does not, then the clouds should be removed before matching the histograms. 12
Resolution Merge Resolution merge resamples low spatial resolution data to a higher spatial resolution based on the finer spatial information provider and retains spectral characteristics carried by the original data. For example, Landsat TM sensors have six spectral bands with 30-m spatial resolution and a panchromatic band with 10-m spatial resolution. Integrating the two types of images can yield a new dataset with 10-m resolution and retain the spectral characteristics of the sensors. Resolution Merge 13
Resolution Merge 14
Spatial Enhancement While radiometric enhancements operate on each pixel individually, spatial enhancement modifies pixel values based on the values of surrounding pixels. 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 enhancement deals largely with spatial frequency. Spatial Enhancement Spatial frequency is defined as the number of changes in brightness value per unit distance for any particular part of an image 1. Zero spatial frequency - a flat image, in which every pixel has the same value 2. Low spatial frequency - an image consisting of a smoothly varying gray scale 3. Highest spatial frequency - an image consisting of a checkerboard of black and white pixels 15
Spatial Enhancement Spatial frequency in remotely sensed imagery may be enhanced or subdued using two different approaches: - Spatial convolution filtering based primarily on the use of convolution masks, and - Fourier analysis which mathematically separates an image into its spatial frequency components. Filtering is a broad term, referring to the altering of spatial or spectral features for image enhancement. Convolution filtering is one method of spatial filtering. Convolution Filtering Convolution filtering is the process of averaging small sets of pixels across an image. Convolution filtering is used to change the spatial frequency characteristics of an image. 16
Convolution filtering is the process of averaging small sets of pixels across an image. Convolution filtering is used to change the spatial frequency characteristics of an image. low-pass filtering Convolution filtering is the process of averaging small sets of pixels across an image. Convolution filtering is used to change the spatial frequency characteristics of an image. High-pass filtering 17
A Spatial convolution kernel is a matrix of numbers that is used to average the value of each pixel with the values of surrounding pixels in a particular way. The numbers in the matrix serve to weight this average toward particular pixels. These numbers are called coefficients, because they are used as such in the mathematical equations. The size of the neighborhood convolution mask or kernel (n) is usually 3 x 3, 5 x 5, 7 x 7, or 9 x 9. An example of 3 x 3 kernel: c1 c2 c3 Mask template = c4 c5 c6 c7 c8 c9 1 1 1 1 1 1 1 1 1 Spatial Convolution Filtering The coefficients, c 1, in the mask are multiplied by the following individual brightness values (BV i ) in the input image: c 1 x BV 1 c 2 x BV 2 c 3 x BV 3 Mask template = c 4 x BV 4 c 5 x BV 5 c 6 x BV 6 c 7 x BV 7 c 8 x BV 8 c 9 x BV 9 The primary input pixel under investigation at any one time is BV 5 = BV i,j 18
Various Convolution Mask Kernels Low-pass filtering: is to de-emphasize or block the high spatial frequency details. 19
Spatial Convolution Filtering: Low Frequency Filter The simplest low-pass filter evaluates a particular input pixel brightness value, BV in, and the pixels surrounding the input pixel, and output a new brightness value, BV out, that is the mean of this convolution. 1 1 1 1 1 1 1 1 1 Low-pass filter: 170 (Averaged) 20
27 3 9 Low Pass Filter 36 4 9 45 5 9 High-pass filtering: is to remove the slowly varying components and enhance the high-frequency local variations. 21
Spatial Convolution Filtering: High-pass (Frequency) Filter One high-frequency filter (HFF 5,out ) is computed by subtracting the output of the low-frequency filter (LFF 5,out ) from twice the value of the original central pixel value, BV 5 : 22
Spatial Convolution Filtering: Edge Enhancement For many remote sensing Earth science applications, the most valuable information that may be derived from an image is contained in the edges surrounding various objects of interest. Edge enhancement delineates these edges and makes the shapes and details comprising the image more conspicuous and perhaps easier to analyze. Edges may be enhanced using either linear or nonlinear edge enhancement techniques. 23
Compass gradient masks may be used to perform two-dimensional, discrete differentiation directional edge enhancement. Spatial Convolution Filtering: Directional First- Difference Linear Edge Enhancement The result of the subtraction can be either negative or possible, therefore a constant, K (usually 127) is added to make all values positive and centered between 0 and 255. 24