Image Enhancement

Applied to more effectively display or record the image data for subsequent visual interpretation.

linear enhancements non-linear enhancements

The objective of the second group of image processing functions grouped under the term of image enhancement, is solely to improve the appearance of the imagery to assist in visual interpretation and analysis. Examples of enhancement functions include contrast stretching toincrease the tonal distinction between various features in a scene, and spatial filtering to enhance (or suppress) specific spatial patterns in an image.

The key to understanding contrast enhancements is to understand the concept of an image histogram.

A histogram is a graphical representation ofthe brightness values that comprise an image. The brightness values (i.e. 0-255) are displayed along the x-axis of the graph. The frequency of occurrence of each of these values in the image is shown on the y-axis.

Linear contrast stretch. This involves identifying lower and upper bounds from the histogram (usually the minimum and maximum brightness values in the image) and applying a transfn to stretch this range to fill the full range.

In our example, the min. value (occupiedby actual data) the histogram is 84 and The max. value is 153. These 70 levels occupyless than of the full 256 levels available. A linear Stretch uniformly expands This small range to Cover the full range of values from 0 to 255.

This enhances the contrast in the image with light toned areas appearing lighter and dark areas appearing darker, making visual interpretation much easier. This graphic illustrates the increase in contrast in an image before (left) and after (right) Linear contrast stretch.

A uniform distribution of the input range of values across the full range may not always be an appropriate enhancement, particularly if the input range is not uniformly distributed.

In this case, a histogram-equalized stretch may be better. This stretch assigns more display values (range) to the frequently occurring portions of the histogram.

In this way, the detail in these areas will be better enhanced

relative to those areas of the original histogram where values occur less frequently.

In other cases, it may be desirable to enhance the contrast in only a specific portion of the histogram.

For example, suppose we have an image of the mouth of a river, and the water portions of the image occupy the digital values from 40 to 76 out of the entire image histogram. If we wished to enhance the detail in the water, perhaps to see variations in sediment load, we could stretch only that small portion of the

histogram represented by the water (40 to 76) to the full grey level range (0 to 255).

All pixels below or above these values would be assigned to 0 and 255, respectively, and the detail in these areas would be lost. However, the detail in the water would be

greatly enhanced.

Spatial filteringEncompasses another set of digital processing functions which are used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency is related to the concept of image texture,It refers to the frequency of the variations in tone that appear in an image.

A low-pass filter is designed to emphasize larger, homogeneous areas of similar tone and reduce the smaller detail in an image. Thus, low-pass filters generally serve to smooth the appearance of an image. Average and median filters, often used for radar imagery, (low-pass filters.) High-pass filters do the opposite and serve to sharpen the appearance of fine detail in an image. One implementation of a high-pass filter first applies a low-pass filter to an image and then subtracts the result from the original, leaving behind only the high spatial frequency information.

Multi-band operations

To enhance or extract features from satellite images which cannot be clearly detected in a single band, you can use the spectral information of the object recorded in multiple bands.

These images may be separate spectral bands from a single multi spectral data set, or they may be individual bands from data sets that have been recorded at different dates or using different sensors. The operations of addition, subtraction, multiplication and division, are performed on two or more co-registered images of the same geographical area. This section deals with multi-band operations. The following operations will be treated:

- The use of ratio images to reduce topographic effects.- Vegetation indexes, some of which are more complex than

ratio's only.- Multi-band statistics.- Principal components analysis.- Image algebra, and;- Image fusion.

Image transformationsWhen a satellite passes over an area with relief, it records both shaded and sunlit areas. These variations in scene illumination conditions are illustrated in the figure. A red silt stone bed shows outcrops on both the sunlit and the shadowed side of a ridge. The observed DNs are substantially lower on the shaded side compared to the sunlit areas. This makes it difficult to follow the silt stone bed around the ridge.

In the individual Landsat-TM bands 3 and 4, the DNs of the silt stone are lower in the shaded than in the sunlit areas. However, the ratio values are nearly identical, irrespective of illumination conditions. Hence, a ratioed image of the scene effectively compensates for the brightness variation, caused by the differences in the topography and emphasizes by the color content of the data.

Image Interpretation

Vegetation IndicesDifferent bands of a multispectral image may be combined to accentuate the vegetated areas. One such combination is the ratio of the near-infrared band to the red band. This ratio is known as the Ratio Vegetation Index (RVI)

RVI = NIR/Red

Since vegetation has high NIR reflectance but low red reflectance, vegetated areas will have higher RVI values compared to non-vegetated areas. Another commonly used vegetation index is the Normalised Difference Vegetation Index (NDVI) computed by

NDVI = (NIR - Red)/(NIR + Red)

Various mathematical combinations of satellite bands, have been found to be sensitive indicators of the presence and condition of green vegetation. These band combinations are thus referred to as vegetation indices. Two such indices are the simple vegetation index (VI) and the normalized difference vegetation index (NDVI).

NDVI = (NIR – Red) / (NIR + Red)

Both are based on the reflectance properties of vegetated areas as compared to clouds,water and snow on the one hand, and rocks and bare soil on the other.

Vegetated areas have a relatively high reflection in the near-infrared and a low reflection in the visible range of the spectrum. Clouds, water and snow have larger visual than near-infrared reflectance. Rock and bare soil have similar reflectance in both spectral regions.

The effect of calculating VI or the NDVI is clearly demonstrated in next table.

Table: Reflectance versus ratio values

TM Band 3 TM Band 4 VI NDVI

Green Vegetation

21 142 121 0.74

Water 21 12 -9 -0.27

Bare soil 123 125 2 0.01

Vegetation maps are produced by generating a normalized difference vegetation index from a infrared image and then doing a vegetation classification. Color infrared photographs collect information in the green, red and near infrared light reflectance spectrum. Green vegetation reflects very strongly in the near infrared light range and therefore infrared images can detect stress in many crops before it is visible with the naked eye

The Normalized Difference Vegetation Index (NDVI) is used to separate green vegetation from the background soil brightness. It is the difference between the near infrared and red reflectance normalized over the sum of these bands.

NDVI = (IR-Red)/(IR+Red)These NDVI maps can then be classified into vegetation categories and displayed as a vegetation maps with different colors representing different levels of vegetation. In the map on the left browns and yellow represent bare soil and shades of green represent vegetation, darker greens are stronger vegetation.

5.3.3 Principal components analysis

Another method (which is not spatial and applied in many fields), called principalcomponents analysis (PCA), can be applied to compact the redundant data into fewer layers. Principal component analysis can be used to transform a set of image bands, as that the new layers (also called components) are not correlated with one another.

These new components are a linear combination of the original bands. Because of this, each component carries new information. The components are ordered in terms of the amount of variance explained, the first two or three components will carry most of the real information of the original data set, while the later components describe only the minor variations (sometimes only noise).

Therefore, only by keeping the first few components most of the information is kept. These components can be used to generate an RGB color composite, in which component 1 is displayed in red, component 2 and 3 in green and blue respectively.

Such an image contains more information than any combination of the three original spectral bands.

To perform PCA, the axis of the spectral space are rotated, the new axis are parallel to the axis of the ellipse (see figure). The length and the direction of the widest transect of the ellipse are calculated. The transect which corresponds to the major (longest) axis of the ellipse, is called the first principal component of the data. The direction of the first principal component is the first eigenvector, and the variance is given by the first eigenvalue.

A new axis of the spectral space is defined by the first principal component. The points in the scatter plot are now given new coordinates, which correspond to this new axis. Since in spectral space, the coordinates of the points are the pixel values, new pixel values are derived and stored in the newly created first principal component.

The second principal component is the widest transect of the ellipse that is orthogonal (perpendicular) to the first principal component. As such, PC2 describes the largest amount of variance that has not yet been described by PC1. In a two dimensional space, PC2 corresponds to the minor axis of the ellipse. In n-dimensions there are n principal components and each new component is consisting of the widest transect which is orthogonal to the previous components.

The output of a PCA contains the following tables:

· Variance/covariance matrix between the original bands (variables).

· Correlation matrix between the original bands.· Component’s eigenvalues – the amount of variance

explained by each of the new bands (%variance = eigenvalue / S[eigenvalue]).

· Eigenvectors – the parameters for the linear combination of the new bands for an inverse transformation, back to the original bands.

· Component’s factor loadings (factor pattern matrix) – factors with a high loading parameter with an original band, have a high correlation with it.

Linear CombinationFor the Morro Bay TM scene there are 7 spectral bands. Thus each pixel has 7 values. The pixel in row i, column j of the image is a vector:

x(i,j,1) x(i,j,2) x(i,j,3) x(i,j,4) x(i,j,5) x(i,j,6) x(i,j,7) x(i,j,1) is the value of band 1 in row i, column j, x(i,j,2) is the value of band 2 in row i, column j, etc. A linear combination of these values, to calculate the first Principal Component, would look like:

                                                                                            

This multiplication and addition is carried out for each of the picture elements, pixels, in the image. The Principal Components Analysis is the calculation of the values of the set of vectors a and then the multiplication of the image data by them to get the projections of the data points onto the Principal Components.

Image Processing and Analysis

3. Classification

• Bands of a single image are used to identify and separate spectral signatures of landscape features.

• Ordination and other statistical techniques are used to “cluster” pixels of similar spectral signatures in a theoretical space.

• The maximum likelihood classifier is most often used.

• Each cluster is then assigned to a category and applied to the image to create a classified image.

• The resulting classified image can now be used and interpreted as a map.

•The resulting classified image will have errors! Accuracy assessment is critical. Maps created by image classification should report an estimate of accuracy.

Image Processing and Analysis

3. Classification

Band 1

Band 2

Band 3

Band 4

BlackBox

Spectral Signatures

Transformation / Clustering

Maximum Likelihood Classifier

Classified Image (Map)

Modeling / GIS

Remote Sensing Images as GIS layers

Remote sensing data (raw or processed) is most powerful when incorporated into a GIS.

Forest Type

Elevation

Soil Depth

Soil pH

Geology Land Ownership

RegressionModel

Substrate and Ownership Mask

Predicted Density of Reeses Buttercup