IDL code for minimum distance calculationNot sure if I missed some obvious CPU brakesK-nearest neighbours in...
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IDL code for minimum distance calculation
Not sure if I missed some obvious CPU brakesK-nearest neighbours in C# for large number of dimensionsParsing and plotting complex numbersSimple multi-dimensional Array class in C++11Pixelating black and white imagesIterating over thousands of objects to generate htmlInterpolating data of multiple trainsRetrieving website categories from a three-level hierarchyGenerating code combinations for matching conditionsdqueue - A dynamic FIFO queue library for C
$begingroup$
I made a program in IDL for performing a minimum distance classification on multispectral satellite data. This is what my main function looks like:
function minimum_distance, stack, exercising_data
;variables
nbands = get_number_of_bands()
nclasses = get_number_of_classes()
x_size = (size(stack, /DIMENSIONS))[0]
y_size = (size(stack, /DIMENSIONS))[1]
result = MAKE_ARRAY(x_size,y_size,/INTEGER)
;calculate class mean for each class
class_means = calculate_class_means(exercising_data)
;find closest class mean for each point and create result image
i = 0
foreach point, stack[0,*,0], i do begin
foreach point, stack[*,i,0], j do begin
print, string(i) + "-" + string(j)
point = REFORM(stack[j,i,*],7,1)
dist_array = [[point],[class_means]]
class = (DISTANCE_MEASURE(dist_array))[0:nclasses-1]
class = min(class,location)
result[j,i] = location
end
end
;prepare result for display
result = result * (255/(nclasses-1))
result = REVERSE(ROTATE(result,2))
return, result
end
The stack is an array of tif files of Landsat 7 data that I load with the read_tiff function. The exercising data looks like this:
;Forst
[ $
[39,25,21,29,14,80,12], $
[40,25,20,28,11,79,12], $
[37,24,20,29,14,81,11], $
[43,28,25,34,14,79,12], $
[44,29,29,40,13,79,13], $
[41,27,23,32,14,80,13], $
[0,0,0,0,0,0,0], $
[46,31,27,36,13,79,12] $
], $
... (more classes)
;Water
[ $
[39,24,19,13,9,92,10], $
[37,22,19,13,9,92,10], $
[38,24,20,13,9,90,10], $
[40,24,22,15,9,89,10], $
[37,22,19,13,10,92,9], $
[38,23,20,13,10,89,9], $
[37,23,18,13,9,90,9], $
[39,25,20,13,9,90,9] $
] $
The program actually works, but doesn't have a good performance. This is due to the nested foreach-loops within the minimum_distance function. With larger satellite images it takes a very long time to process. I can imagine that you can reach a much better performance using some IDL function that I don't know of. Do you know a way in how I can give this program a better performance and replace the foreach-loops?
If you want to take a look at the complete program, here it is.
performance iteration interactive-data-language
edited 12 mins ago
Jamal♦
30.3k11119227
asked Jan 29 at 10:18
TheKidsWantDjentTheKidsWantDjent
1161
$endgroup$
add a comment |
$begingroup$
I made a program in IDL for performing a minimum distance classification on multispectral satellite data. This is what my main function looks like:
function minimum_distance, stack, exercising_data
;variables
nbands = get_number_of_bands()
nclasses = get_number_of_classes()
x_size = (size(stack, /DIMENSIONS))[0]
y_size = (size(stack, /DIMENSIONS))[1]
result = MAKE_ARRAY(x_size,y_size,/INTEGER)
;calculate class mean for each class
class_means = calculate_class_means(exercising_data)
;find closest class mean for each point and create result image
i = 0
foreach point, stack[0,*,0], i do begin
foreach point, stack[*,i,0], j do begin
print, string(i) + "-" + string(j)
point = REFORM(stack[j,i,*],7,1)
dist_array = [[point],[class_means]]
class = (DISTANCE_MEASURE(dist_array))[0:nclasses-1]
class = min(class,location)
result[j,i] = location
end
end
;prepare result for display
result = result * (255/(nclasses-1))
result = REVERSE(ROTATE(result,2))
return, result
end
The stack is an array of tif files of Landsat 7 data that I load with the read_tiff function. The exercising data looks like this:
;Forst
[ $
[39,25,21,29,14,80,12], $
[40,25,20,28,11,79,12], $
[37,24,20,29,14,81,11], $
[43,28,25,34,14,79,12], $
[44,29,29,40,13,79,13], $
[41,27,23,32,14,80,13], $
[0,0,0,0,0,0,0], $
[46,31,27,36,13,79,12] $
], $
... (more classes)
;Water
[ $
[39,24,19,13,9,92,10], $
[37,22,19,13,9,92,10], $
[38,24,20,13,9,90,10], $
[40,24,22,15,9,89,10], $
[37,22,19,13,10,92,9], $
[38,23,20,13,10,89,9], $
[37,23,18,13,9,90,9], $
[39,25,20,13,9,90,9] $
] $
The program actually works, but doesn't have a good performance. This is due to the nested foreach-loops within the minimum_distance function. With larger satellite images it takes a very long time to process. I can imagine that you can reach a much better performance using some IDL function that I don't know of. Do you know a way in how I can give this program a better performance and replace the foreach-loops?
If you want to take a look at the complete program, here it is.
performance iteration interactive-data-language
edited 12 mins ago
Jamal♦
30.3k11119227
asked Jan 29 at 10:18
TheKidsWantDjentTheKidsWantDjent
1161
$endgroup$
add a comment |
$begingroup$
I made a program in IDL for performing a minimum distance classification on multispectral satellite data. This is what my main function looks like:
function minimum_distance, stack, exercising_data
;variables
nbands = get_number_of_bands()
nclasses = get_number_of_classes()
x_size = (size(stack, /DIMENSIONS))[0]
y_size = (size(stack, /DIMENSIONS))[1]
result = MAKE_ARRAY(x_size,y_size,/INTEGER)
;calculate class mean for each class
class_means = calculate_class_means(exercising_data)
;find closest class mean for each point and create result image
i = 0
foreach point, stack[0,*,0], i do begin
foreach point, stack[*,i,0], j do begin
print, string(i) + "-" + string(j)
point = REFORM(stack[j,i,*],7,1)
dist_array = [[point],[class_means]]
class = (DISTANCE_MEASURE(dist_array))[0:nclasses-1]
class = min(class,location)
result[j,i] = location
end
end
;prepare result for display
result = result * (255/(nclasses-1))
result = REVERSE(ROTATE(result,2))
return, result
end
The stack is an array of tif files of Landsat 7 data that I load with the read_tiff function. The exercising data looks like this:
;Forst
[ $
[39,25,21,29,14,80,12], $
[40,25,20,28,11,79,12], $
[37,24,20,29,14,81,11], $
[43,28,25,34,14,79,12], $
[44,29,29,40,13,79,13], $
[41,27,23,32,14,80,13], $
[0,0,0,0,0,0,0], $
[46,31,27,36,13,79,12] $
], $
... (more classes)
;Water
[ $
[39,24,19,13,9,92,10], $
[37,22,19,13,9,92,10], $
[38,24,20,13,9,90,10], $
[40,24,22,15,9,89,10], $
[37,22,19,13,10,92,9], $
[38,23,20,13,10,89,9], $
[37,23,18,13,9,90,9], $
[39,25,20,13,9,90,9] $
] $
The program actually works, but doesn't have a good performance. This is due to the nested foreach-loops within the minimum_distance function. With larger satellite images it takes a very long time to process. I can imagine that you can reach a much better performance using some IDL function that I don't know of. Do you know a way in how I can give this program a better performance and replace the foreach-loops?
If you want to take a look at the complete program, here it is.
performance iteration interactive-data-language
edited 12 mins ago
Jamal♦
30.3k11119227
asked Jan 29 at 10:18
TheKidsWantDjentTheKidsWantDjent
1161
$endgroup$
I made a program in IDL for performing a minimum distance classification on multispectral satellite data. This is what my main function looks like:
function minimum_distance, stack, exercising_data
;variables
nbands = get_number_of_bands()
nclasses = get_number_of_classes()
x_size = (size(stack, /DIMENSIONS))[0]
y_size = (size(stack, /DIMENSIONS))[1]
result = MAKE_ARRAY(x_size,y_size,/INTEGER)
;calculate class mean for each class
class_means = calculate_class_means(exercising_data)
;find closest class mean for each point and create result image
i = 0
foreach point, stack[0,*,0], i do begin
foreach point, stack[*,i,0], j do begin
print, string(i) + "-" + string(j)
point = REFORM(stack[j,i,*],7,1)
dist_array = [[point],[class_means]]
class = (DISTANCE_MEASURE(dist_array))[0:nclasses-1]
class = min(class,location)
result[j,i] = location
end
end
;prepare result for display
result = result * (255/(nclasses-1))
result = REVERSE(ROTATE(result,2))
return, result
end
The stack is an array of tif files of Landsat 7 data that I load with the read_tiff function. The exercising data looks like this:
;Forst
[ $
[39,25,21,29,14,80,12], $
[40,25,20,28,11,79,12], $
[37,24,20,29,14,81,11], $
[43,28,25,34,14,79,12], $
[44,29,29,40,13,79,13], $
[41,27,23,32,14,80,13], $
[0,0,0,0,0,0,0], $
[46,31,27,36,13,79,12] $
], $
... (more classes)
;Water
[ $
[39,24,19,13,9,92,10], $
[37,22,19,13,9,92,10], $
[38,24,20,13,9,90,10], $
[40,24,22,15,9,89,10], $
[37,22,19,13,10,92,9], $
[38,23,20,13,10,89,9], $
[37,23,18,13,9,90,9], $
[39,25,20,13,9,90,9] $
] $
The program actually works, but doesn't have a good performance. This is due to the nested foreach-loops within the minimum_distance function. With larger satellite images it takes a very long time to process. I can imagine that you can reach a much better performance using some IDL function that I don't know of. Do you know a way in how I can give this program a better performance and replace the foreach-loops?
If you want to take a look at the complete program, here it is.
performance iteration interactive-data-language
performance iteration interactive-data-language
edited 12 mins ago
Jamal♦
30.3k11119227
asked Jan 29 at 10:18
TheKidsWantDjentTheKidsWantDjent
1161
edited 12 mins ago
Jamal♦
30.3k11119227
asked Jan 29 at 10:18
TheKidsWantDjentTheKidsWantDjent
1161
edited 12 mins ago
Jamal♦
30.3k11119227
edited 12 mins ago
Jamal♦
30.3k11119227
edited 12 mins ago
Jamal♦
30.3k11119227
30.3k11119227
asked Jan 29 at 10:18
TheKidsWantDjentTheKidsWantDjent
1161
asked Jan 29 at 10:18
TheKidsWantDjentTheKidsWantDjent
1161
asked Jan 29 at 10:18
TheKidsWantDjentTheKidsWantDjent
1161
1161
add a comment |
add a comment |
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