matlabimage-processinginterpolationbicubicbilinear-interpolation
Bilinear and bicubic interpolation of 2D matrix in matlab
I am trying to apply Bilinear and bicubic interpolation on my data set which is stored in text files. These text files are in a folder and are named output_00.text to output_23.text. Each text file consist of three columns. First is Latitude, second is longitude and third column is temperature value at this latitude and longitude (position over earth).
Temperature column contain -9999.000 as not a number or NaN values. This NaN value appears for random rows in each file.
I want to interpolate these NaN values with bilinear/ bicubic interpolation technique.This code will read each text file and interpolate it with bilinear method and save it with method_00.text.
My one text file look like this
21.500 60.500 295.867
21.500 61.500 295.828
21.500 62.500 295.828
21.500 63.500 295.867
21.500 64.500 296.102
21.500 65.500 296.234
21.500 66.500 296.352
21.500 67.500 296.336
21.500 68.500 296.305
21.500 69.500 298.281
21.500 70.500 301.828
21.500 71.500 302.094
21.500 72.500 299.469
21.500 73.500 301.711
21.500 74.500 -9999.000
21.500 75.500 -9999.000
21.500 76.500 -9999.000
21.500 77.500 -9999.000
21.500 78.500 -9999.000
22.500 60.500 295.477
22.500 61.500 295.484
22.500 62.500 295.516
22.500 63.500 295.547
22.500 64.500 295.852
22.500 65.500 295.859
22.500 66.500 295.852
22.500 67.500 295.711
22.500 68.500 295.969
22.500 69.500 298.562
22.500 70.500 300.828
22.500 71.500 302.352
22.500 72.500 300.570
22.500 73.500 301.383
22.500 74.500 -9999.000
22.500 75.500 -9999.000
22.500 76.500 -9999.000
22.500 77.500 -9999.000
22.500 78.500 -9999.000
23.500 60.500 294.906
23.500 61.500 294.898
23.500 62.500 295.000
23.500 63.500 295.078
23.500 64.500 295.297
23.500 65.500 295.359
23.500 66.500 295.297
23.500 67.500 295.312
23.500 68.500 296.664
23.500 69.500 298.781
23.500 70.500 299.211
23.500 71.500 300.109
23.500 72.500 301.000
23.500 73.500 301.594
23.500 74.500 302.000
23.500 75.500 -9999.000
23.500 76.500 -9999.000
23.500 77.500 -9999.000
23.500 78.500 -9999.000
24.500 60.500 294.578
24.500 61.500 294.516
24.500 62.500 294.734
24.500 63.500 294.789
24.500 64.500 294.844
24.500 65.500 294.562
24.500 66.500 294.734
24.500 67.500 296.367
24.500 68.500 297.438
24.500 69.500 298.531
24.500 70.500 298.453
24.500 71.500 299.195
24.500 72.500 300.062
24.500 73.500 -9999.000
24.500 74.500 -9999.000
24.500 75.500 -9999.000
24.500 76.500 -9999.000
24.500 77.500 -9999.000
24.500 78.500 -9999.000
25.500 60.500 296.258
25.500 61.500 296.391
25.500 62.500 296.672
25.500 63.500 296.398
25.500 64.500 295.773
25.500 65.500 295.812
25.500 66.500 296.609
25.500 67.500 297.977
25.500 68.500 297.109
25.500 69.500 297.828
25.500 70.500 298.430
25.500 71.500 298.836
25.500 72.500 298.703
25.500 73.500 -9999.000
25.500 74.500 -9999.000
25.500 75.500 -9999.000
25.500 76.500 -9999.000
25.500 77.500 -9999.000
25.500 78.500 299.023
26.500 60.500 -9999.000
26.500 61.500 298.266
26.500 62.500 296.773
26.500 63.500 -9999.000
26.500 64.500 -9999.000
26.500 65.500 -9999.000
26.500 66.500 297.250
26.500 67.500 296.188
26.500 68.500 295.938
26.500 69.500 296.906
26.500 70.500 297.828
26.500 71.500 299.312
26.500 72.500 299.359
26.500 73.500 -9999.000
26.500 74.500 -9999.000
26.500 75.500 -9999.000
26.500 76.500 -9999.000
26.500 77.500 298.875
26.500 78.500 296.773
27.500 60.500 -9999.000
27.500 61.500 -9999.000
27.500 62.500 -9999.000
27.500 63.500 -9999.000
27.500 64.500 -9999.000
27.500 65.500 -9999.000
27.500 66.500 -9999.000
27.500 67.500 295.352
27.500 68.500 295.148
27.500 69.500 295.750
27.500 70.500 295.750
27.500 71.500 296.070
27.500 72.500 295.227
27.500 73.500 -9999.000
27.500 74.500 -9999.000
27.500 75.500 -9999.000
27.500 76.500 -9999.000
27.500 77.500 -9999.000
27.500 78.500 296.609
28.500 60.500 -9999.000
28.500 61.500 -9999.000
28.500 62.500 -9999.000
28.500 63.500 -9999.000
28.500 64.500 -9999.000
28.500 65.500 -9999.000
28.500 66.500 -9999.000
28.500 67.500 295.773
28.500 68.500 295.375
28.500 69.500 295.438
28.500 70.500 294.664
28.500 71.500 294.906
28.500 72.500 294.812
28.500 73.500 295.805
28.500 74.500 -9999.000
28.500 75.500 -9999.000
28.500 76.500 -9999.000
28.500 77.500 -9999.000
28.500 78.500 -9999.000
29.500 60.500 -9999.000
29.500 61.500 -9999.000
29.500 62.500 -9999.000
29.500 63.500 -9999.000
29.500 64.500 -9999.000
29.500 65.500 -9999.000
29.500 66.500 -9999.000
29.500 67.500 295.719
29.500 68.500 296.797
29.500 69.500 293.375
29.500 70.500 294.305
29.500 71.500 294.070
29.500 72.500 293.750
29.500 73.500 295.539
29.500 74.500 -9999.000
29.500 75.500 -9999.000
29.500 76.500 -9999.000
29.500 77.500 -9999.000
29.500 78.500 -9999.000
30.500 60.500 -9999.000
30.500 61.500 -9999.000
30.500 62.500 -9999.000
30.500 63.500 -9999.000
30.500 64.500 -9999.000
30.500 65.500 -9999.000
30.500 66.500 -9999.000
30.500 67.500 -9999.000
30.500 68.500 -9999.000
30.500 69.500 -9999.000
30.500 70.500 293.320
30.500 71.500 292.930
30.500 72.500 293.570
30.500 73.500 294.648
30.500 74.500 295.383
30.500 75.500 -9999.000
30.500 76.500 -9999.000
30.500 77.500 -9999.000
30.500 78.500 -9999.000
31.500 60.500 -9999.000
31.500 61.500 -9999.000
31.500 62.500 -9999.000
31.500 63.500 -9999.000
31.500 64.500 -9999.000
31.500 65.500 -9999.000
31.500 66.500 -9999.000
31.500 67.500 -9999.000
31.500 68.500 -9999.000
31.500 69.500 -9999.000
31.500 70.500 293.992
31.500 71.500 293.422
31.500 72.500 294.438
31.500 73.500 294.141
31.500 74.500 -9999.000
31.500 75.500 -9999.000
31.500 76.500 -9999.000
31.500 77.500 -9999.000
31.500 78.500 -9999.000
32.500 60.500 -9999.000
32.500 61.500 -9999.000
32.500 62.500 -9999.000
32.500 63.500 -9999.000
32.500 64.500 -9999.000
32.500 65.500 -9999.000
32.500 66.500 -9999.000
32.500 67.500 -9999.000
32.500 68.500 -9999.000
32.500 69.500 -9999.000
32.500 70.500 -9999.000
32.500 71.500 294.312
32.500 72.500 294.812
32.500 73.500 -9999.000
32.500 74.500 -9999.000
32.500 75.500 -9999.000
32.500 76.500 -9999.000
32.500 77.500 -9999.000
32.500 78.500 -9999.000
33.500 60.500 -9999.000
33.500 61.500 -9999.000
33.500 62.500 -9999.000
33.500 63.500 -9999.000
33.500 64.500 -9999.000
33.500 65.500 -9999.000
33.500 66.500 -9999.000
33.500 67.500 -9999.000
33.500 68.500 -9999.000
33.500 69.500 -9999.000
33.500 70.500 -9999.000
33.500 71.500 -9999.000
33.500 72.500 -9999.000
33.500 73.500 -9999.000
33.500 74.500 -9999.000
33.500 75.500 -9999.000
33.500 76.500 -9999.000
33.500 77.500 -9999.000
33.500 78.500 -9999.000
34.500 60.500 -9999.000
34.500 61.500 -9999.000
34.500 62.500 -9999.000
34.500 63.500 -9999.000
34.500 64.500 -9999.000
34.500 65.500 -9999.000
34.500 66.500 -9999.000
34.500 67.500 -9999.000
34.500 68.500 -9999.000
34.500 69.500 -9999.000
34.500 70.500 -9999.000
34.500 71.500 -9999.000
34.500 72.500 -9999.000
34.500 73.500 -9999.000
34.500 74.500 -9999.000
34.500 75.500 -9999.000
34.500 76.500 -9999.000
34.500 77.500 -9999.000
34.500 78.500 -9999.000
35.500 60.500 -9999.000
35.500 61.500 -9999.000
35.500 62.500 -9999.000
35.500 63.500 -9999.000
35.500 64.500 -9999.000
35.500 65.500 -9999.000
35.500 66.500 -9999.000
35.500 67.500 -9999.000
35.500 68.500 -9999.000
35.500 69.500 -9999.000
35.500 70.500 -9999.000
35.500 71.500 -9999.000
35.500 72.500 -9999.000
35.500 73.500 -9999.000
35.500 74.500 -9999.000
35.500 75.500 -9999.000
35.500 76.500 -9999.000
35.500 77.500 -9999.000
35.500 78.500 -9999.000
36.500 60.500 276.742
36.500 61.500 274.406
36.500 62.500 -9999.000
36.500 63.500 -9999.000
36.500 64.500 -9999.000
36.500 65.500 272.219
36.500 66.500 273.023
36.500 67.500 275.875
36.500 68.500 -9999.000
36.500 69.500 -9999.000
36.500 70.500 -9999.000
36.500 71.500 -9999.000
36.500 72.500 -9999.000
36.500 73.500 -9999.000
36.500 74.500 -9999.000
36.500 75.500 -9999.000
36.500 76.500 -9999.000
36.500 77.500 -9999.000
36.500 78.500 -9999.000
37.500 60.500 277.406
37.500 61.500 277.547
37.500 62.500 276.375
37.500 63.500 275.484
37.500 64.500 276.820
37.500 65.500 275.312
37.500 66.500 274.875
37.500 67.500 275.875
37.500 68.500 -9999.000
37.500 69.500 -9999.000
37.500 70.500 -9999.000
37.500 71.500 -9999.000
37.500 72.500 -9999.000
37.500 73.500 -9999.000
37.500 74.500 -9999.000
37.500 75.500 -9999.000
37.500 76.500 -9999.000
37.500 77.500 -9999.000
37.500 78.500 -9999.000
On google i found a tool for bilinear interpolation over a image or matrix. For the time shake this code can be modify to my requirement. But How?
function interVal=interpImg(img,yx,zpad)
% BiLinear interpolation using 4 pixels around the target location with ceil convention
% RGB = 1 for gray scale images.
% img can be a single layer matrix or a RGB layer colored image
% yx =[y_value, x_value]; It can be either horizontal or vertical vector
%
% zpad is a boolean variable. if true, zeros are used for pixel values
% outside of the given img. If false, the nearest edge value is repeated.
%
% Example:
% [m,n]=meshgrid(1:3);img=[m+n]
% --> 2 3 4
% 3 4 5
% 4 5 6
% interpImg(img,[2.4,2.2])
% --> 4.6
%
% Disi A, Sep,16th,2013
% [email protected]
if nargin<4,RGB=ndims(img);RGB(RGB<3)=1; end
if nargin<3,zpad=true; end
yx0=floor(yx);
wt=yx-yx0; wtConj=1-wt;
interTop=wtConj(2)*pixLookup(img,yx0(1),yx0(2),zpad,RGB)+wt(2)*pixLookup(img,yx0(1),yx(2),zpad,RGB);
interBtm=wtConj(2)*pixLookup(img,yx(1),yx0(2),zpad,RGB)+wt(2)*pixLookup(img,yx(1),yx(2),zpad,RGB);
interVal=wtConj(1)*interTop+wt(1)*interBtm;
end
function pixVal=pixLookup(img,y,x,zpad,RGB)
% This helper function looks up a pixel value from a given input image
% img is the input image (RGB or Grayscale)
% yx is the coordinate and repEdge tells the condition for pixel values out
% side of img (Use round up convention)
% For grayscale use RGB =1
if nargin<4,RGB=3;end
pixVal=zeros(1,1,RGB); %Initialize the pixel
if nargin<3
zpad=true; %pad with black pixels
end
if RGB==3
[ROW,COL,~]=size(img);
else
[ROW,COL]=size(img);
end
% If the pixel value is outside of image given
if (x<=0)||(x>COL)||(y<=0)||(y>ROW)
if zpad
pixVal(:)=0;
else
y0=y;x0=x;
y0(y0<1)=1; x0(x0<1)=1;
y0(y0>ROW)=ROW;x0(x0>COL)=COL;
pixVal=img(y0,x0,:);
end
else
pixVal=img(ceil(y),ceil(x),:);
end
end
. Link of this tool is here http://www.mathworks.com/matlabcentral/fileexchange/43533-bilinear-interpolation-of-an-image-or-matrix
Solution
You're going to want to read in the data, identify all the rows where the last column is unknown. Then using the "good" data points you can construct a 2D interpolant (f(x,y)) to sample at the unknown points.
You'll have to use griddata rather than interp2 since your data is scattered. You'll want to use the linear interpolation method (the default).
The code below should achieve this result.
% Read in data from file into an M x 3 matrix
fid = fopen('data.txt', 'rb');
data = textscan(fid, '%f %f %f');
data = cat(2, data{:});
% Find rows where third column is unknown
isUnknown = data(:,3) == -9999;
isKnown = ~isUnknown;
% Perform scattered interpolation AT unknown location USING known locations
newValues = griddata(data(isKnown,1), data(isKnown,2), data(isKnown,3), ...
data(isUnknown,1), data(isUnknown, 2), 'linear');
% Replace the unknown values with the interpolated values
data(isUnknown, 3) = newValues;
If we visualize this we will see the following.
Unfortunately, a lot of your empty data values actually lie outside of the range of the data that you know. griddata will fill these in with NaNs. In order to fill these in, you can't just rely on interpolation but rather must try to use extrapolation.
If you actually want to do this (rather than leaving them as NaN's), you will want to use scatteredInterpolant with linear extrapolation.
interpolant = scatteredInterpolant(data(isKnown,1:2), data(isKnown,3), 'linear', 'linear');
newValues = interpolant(data(isUnknown,1:2));
data(isUnknown,3) = newValues;
Now you can see that those values have been extrapolated.
You should choose whichever method is more appropriate for your use case.
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