MultispectralImageEnhancementExample.pdf

1Enhancing MultispectralColor Composite Images

Table of ContentsStep 1: Construct Truecolor Composite from a Multispectral Image ............................................. 1Step 2: Use Histograms to Explore Un-Enhanced Truecolor Composite ........................................ 2Step 3: Use Correlation to Explore Un-Enhanced Truecolor Composite ......................................... 3Step 4: Enhance Truecolor Composite with a Contrast Stretch .................................................... 4Step 5: Check Histogram Following the Contrast Stretch ........................................................... 5Step 6: Enhance Truecolor Composite with a Decorrelation Stretch .............................................. 6Step 7: Check Correlation Following the Decorrelation Stretch ................................................... 7Step 8: Construct and Enhance a CIR Composite ...................................................................... 8

This example shows some basic image composition and enhancement techniques for use with multispectral data. Itis often necessary to enhance multispectral radiance or reflectance data to create an image that is suitable for visualinterpretation. The example uses Landsat thematic mapper imagery covering part of Paris, France. Seven spectralbands are stored in one file in the Erdas LAN format. Concepts covered include:

Reading multispectral data from Erdas LAN files

Constructing color composites from different band combinations

Enhancing imagery with a contrast stretch

Enhancing imagery with a decorrelation stretch

Using scatterplots

Step 1: Construct Truecolor Composite from aMultispectral Image

The LAN file, paris.lan, contains a 7-band 512-by-512 Landsat image. A 128-byte header is followedby the pixel values, which are band interleaved by line (BIL) in order of increasing band number. Theyare stored as unsigned 8-bit integers, in little-endian byte order.

Read bands 3, 2, and 1 from the LAN file using the MATLAB function multibandread. Thesebands cover the visible part of the spectrum. When they are mapped to the red, green, and blue planes,respectively, of an RGB image, the result is a standard truecolor composite. The final input argument tomultibandread specifies which bands to read, and in which order, so that you can construct an RGBcomposite in a single step.

truecolor = multibandread('paris.lan', [512, 512, 7], 'uint8=>uint8', ... 128, 'bil', 'ieee-le', {'Band','Direct',[3 2 1]});

The truecolor composite has very little contrast and the colors are unbalanced.

figure

Enhancing MultispectralColor Composite Images

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imshow(truecolor);title('Truecolor Composite (Un-enhanced)')text(size(truecolor,2), size(truecolor,1) + 15,... 'Image courtesy of Space Imaging, LLC',... 'FontSize', 7, 'HorizontalAlignment', 'right')

Step 2: Use Histograms to Explore Un-En-hanced Truecolor Composite

By viewing a histogram of the red band, for example, you can see that the data is concentrated within asmall part of the available dynamic range. This is one reason why the truecolor composite appears dull.

figureimhist(truecolor(:,:,1))title('Histogram of the Red Band (Band 3)')


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Step 3: Use Correlation to Explore Un-En-hanced Truecolor Composite

Another reason for the dull appearance of the composite is that the visible bands are highly correlatedwith each other. Two- and three-band scatterplots are an excellent way to gauge the degree of correlationamong spectral bands. You can make them easily just by using plot.

r = truecolor(:,:,1);g = truecolor(:,:,2);b = truecolor(:,:,3);figureplot3(r(:),g(:),b(:),'.')grid('on')xlabel('Red (Band 3)')ylabel('Green (Band 2)')zlabel('Blue (Band 1)')title('Scatterplot of the Visible Bands')


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The pronounced linear trend of the red-green-blue scatterplot indicates that the visible bands are highlycorrelated. This helps explain the monochromatic look of the un-enhanced truecolor composite.

Step 4: Enhance Truecolor Composite with aContrast Stretch

When you use imadjust to apply a linear contrast stretch to the truecolor composite image, the surfacefeatures are easier to recognize.

stretched_truecolor = imadjust(truecolor,stretchlim(truecolor));figureimshow(stretched_truecolor)title('Truecolor Composite after Contrast Stretch')


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Step 5: Check Histogram Following the Con-trast Stretch

A histogram of the red band after applying a contrast stretch shows that the data has been spread overmuch more of the available dynamic range.

figureimhist(stretched_truecolor(:,:,1))title('Histogram of Red Band (Band 3) after Contrast Stretch')


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Step 6: Enhance Truecolor Composite with aDecorrelation Stretch

Another way to enhance the truecolor composite is to use a decorrelation stretch, which enhances colorseparation across highly correlated channels. Use decorrstretch to perform the decorrelation stretch(followed by a linear contrast stretch, as specified by the optional parameter-value pair 'Tol' and 0.1).

decorrstretched_truecolor = decorrstretch(truecolor, 'Tol', 0.01);figureimshow(decorrstretched_truecolor)title('Truecolor Composite after Decorrelation Stretch')


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Again, surface features have become much more clearly visible, but in a different way. The spectral dif-ferences across the scene have been exaggerated. A noticeable example is the area of green on the leftedge, which appears black in the contrast-stretched composite. This green area is the Bois de Boulogne,a large park on the western edge of Paris.

Step 7: Check Correlation Following the Decor-relation Stretch

As expected, a scatterplot following the decorrelation stretch shows a strong decrease in correlation.

r = decorrstretched_truecolor(:,:,1);g = decorrstretched_truecolor(:,:,2);b = decorrstretched_truecolor(:,:,3);figureplot3(r(:),g(:),b(:),'.')grid('on')xlabel('Red (Band 3)')ylabel('Green (Band 2)')zlabel('Blue (Band 1)')title('Scatterplot of the Visible Bands after Decorrelation Stretch')


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Step 8: Construct and Enhance a CIR Compos-ite

Just as with the visible bands, information from Landsat bands covering non-visible portions of the spec-trum can be viewed by constructing and enhancing RGB composite images. The near infrared (NIR) band(Band 4) is important because of the high reflectance of chlorophyll in this part of the spectrum. It is evenmore useful when combined with visible red and green (Bands 3 and 2, respectively) to form a color in-frared (CIR) composite image. Color infrared (CIR) composites are commonly used to identify vegetationor assess its state of growth and/or health.

Construct a CIR composite by reading from the original LAN file and composing an RGB image that mapsbands 4, 3, and 2 to red, green, and blue, respectively.

CIR = multibandread('paris.lan', [512, 512, 7], 'uint8=>uint8', ... 128, 'bil', 'ieee-le', {'Band','Direct',[4 3 2]});

Even though the near infrared (NIR) band (Band 4) is less correlated with the visible bands than the visiblebands are with each other, a decorrelation stretch makes many features easier to see.

stretched_CIR = decorrstretch(CIR, 'Tol', 0.01);figureimshow(stretched_CIR)title('CIR after Decorrelation Stretch')


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A property of color infrared composites is that they look red in areas with a high vegetation (chlorophyll)density. Notice that the Bois de Boulogne park is red in the CIR composite, which is consistent with itsgreen appearance in the decorrelation-stretched truecolor composite.

See also decorrstretch, imhist, imadjust, multibandread, stretchlim.

Copyright 1993-2013 The MathWorks, Inc.Published with MATLAB R2014b

Table of ContentsStep 1: Construct Truecolor Composite from a Multispectral ImageStep 2: Use Histograms to Explore Un-Enhanced Truecolor CompositeStep 3: Use Correlation to Explore Un-Enhanced Truecolor CompositeStep 4: Enhance Truecolor Composite with a Contrast StretchStep 5: Check Histogram Following the Contrast StretchStep 6: Enhance Truecolor Composite with a Decorrelation StretchStep 7: Check Correlation Following the Decorrelation StretchStep 8: Construct and Enhance a CIR Composite

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MultispectralImageEnhancementExample.pdf