I = imread('C:\xxx.jpg');
rgb_percent = squeeze(sum(sum(I,1),2))/sum(I(:))*100
rgb_percent =
27.3365
34.4873
38.1762
rgb_percent = squeeze(sum(sum(I,1),2))/sum(I(:))*100
rgb_percent =
27.3365
34.4873
38.1762
RGB band combination of Satellite image data (MATLAB code)
RGB band combination
%
---------------------------- VNIR or CIR --------------------------------
%
Pull bands 3N, 2, and 1 from the HDF file into their own matrices. These
%
bands are in the VNIR (aka CIR) range and will highlight actively
%
photosynthesizing vegetation (showns as red), undisturbed bedrock and
%
soils (browns, greens, and greys), and the built environment (blues,
%
greens, reddish-purples, and whites)
Band_3
= hdfread('SPCraterASTERData.hdf','VNIR_Swath','Fields','ImageData3N');
Band_2
= hdfread('SPCraterASTERData.hdf','VNIR_Swath','Fields','ImageData2');
Band_1
= hdfread('SPCraterASTERData.hdf','VNIR_Swath','Fields','ImageData1');
%
Construct an RGB matrix from the three bands into one 3D matrix.
SPCrater_321
= cat(3,Band_3,Band_2,Band_1);
%
Display the image
figure(1)
imshow(SPCrater_321)
xlabel('Longitude
(pixels)')
ylabel('Latitude
(pixels)')
title('ASTER
Bands 3, 2, 1')
shortwave infrared of Satellite image data (MATLAB code)
Here is the shortwave infrared
(SWIR):
%
---------------------------- SWIR ---------------------------------------
%
Pull bands 8, 6, and 4 from the HDF file into their own matrices. These
%
bands are in the SWIR range and will highlight spectral features
%
diagnostic for iron oxides, illite, kaolinite, and carbonates.
Band_8
= hdfread('SPCraterASTERData.hdf','SWIR_Swath','Fields','ImageData8');
Band_6
= hdfread('SPCraterASTERData.hdf','SWIR_Swath','Fields','ImageData6');
Band_4
= hdfread('SPCraterASTERData.hdf','SWIR_Swath','Fields','ImageData4');
%
Construct an RGB matrix from the three bands into one 3D matrix.
SPCrater_864
= cat(3 , Band_8 , Band_6 , Band_4);
%
Perform histogram equalization stretch on the image
for
i=1:3
SPCrater_864_stretch(:,:,i)=histeq(SPCrater_864(:,:,i));
end
%
Display the image
figure(3)
imshow(SPCrater_864_stretch)
xlabel('Longitude
(pixels)')
ylabel('Latitude
(pixels)')
title('ASTER
Bands 8, 6, 4')
Thermal Infrared of Satellite image data (MATLAB code)
Thermal Infrared:
%
-------------------------- TIR ------------------------------------------
%
Pull bands 13, 12, and 10 from the HDF file into their own matrices.
%
These bands are in the TIR range and will highlight spectral features
%
diagnostic for silicates, iron- and magnesium-bearing minerals and rocks,
%
and carbonates. Quartzites are bright red, basaltic rocks are blue,
%
granitoids are purple-violet, and carbonates are greenish yellow.
Band_13
= hdfread('SPCraterASTERData.hdf','TIR_Swath','Fields','ImageData13');
Band_12
= hdfread('SPCraterASTERData.hdf','TIR_Swath','Fields','ImageData12');
Band_10
= hdfread('SPCraterASTERData.hdf','TIR_Swath','Fields','ImageData10');
%
Construct an RGB matrix from the three bands into one 3D matrix.
SPCrater_131210
= cat(3 , Band_13 , Band_12 , Band_10);
%
Do a decorrelation stretch to enhance differences in colors.
SPCrater_131210_decorr
= decorrstretch(SPCrater_131210,'Tol',0.01);
%
Display the image
figure(4)
imshow(SPCrater_131210_decorr)
xlabel('Longitude
(pixels)')
ylabel('Latitude
(pixels)')
title('ASTER
Bands 13, 12, 10')
Normalized Difference Vegetation Index of Satellite Image data (MATLAB code)
Normalized Difference Vegetation
Index is a single band image derived from mathematical combinations of bands
which measure the responce of vegetation very differently. This image is a
measure of relative amounts of green vegetation. The NDVI is computed as: (Band_3
- Band_2) ./ (Band_3 + Band_2).
%
---------------------------- NDVI ---------------------------------------
%
Compute the Normalized Difference Vegetation Index (NDVI) using the following
%
formula: [(Band_3 - Band_2)./(Band_3 + Band_2)]. The NDVI shows the relative
%
abundance of actively photosynthesizing vegetation. In other words, it
%
assesses how healthy the vegetation is!
%
Pull the bands.
Band_3
= hdfread('SPCraterASTERData.hdf','VNIR_Swath','Fields','ImageData3N');
Band_2
= hdfread('SPCraterASTERData.hdf','VNIR_Swath','Fields','ImageData2');
Band_1
= hdfread('SPCraterASTERData.hdf','VNIR_Swath','Fields','ImageData1');
%
Calculate NDVI.
%
Before we can calculate this, let's look at the format (aka "class"
of
%
Bands 1, 2, and 3 in the MATLAB Workspace. You will notice that they are
%
"uint8", which means they are 8-bit integers! This isn't useful to
us,
%
especially if we want to do division because division of integers can
%
only result in whole numbers. So, we first need to convert these bands to
%
a class known as "single" so that we can do some band ratioing!
Band_2
= im2single(Band_2);
Band_3
= im2single(Band_3);
SPCrater_NDVI
= (Band_3 - Band_2) ./ (Band_3 + Band_2);
%
Display the image.
figure(5)
%
Note that because the range of numbers in our NDVI ratio is between
%
0 and 1, we need to display the image's colors using an appropriate scale
%
[0 1]
imshow(SPCrater_NDVI,'DisplayRange',[0
1])
xlabel('Longitude
(pixels)')
ylabel('Latitude
(pixels)')
title('NDVI
computed from ASTER Image')
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