L4 & 5 imagery is plagued by banding issues that are especially noticeable over low contrast snow and ice surfaces. This causes the feature tracking to lock onto banding discontinuities instead of surface features. To reduce the influence of banding we need to implement an fft filter that deconstructs the image, removes power in the across-track image direction then reforms the image.
Here is a Matlab that removes the banding:
function Ix = arImgFFT(validDomain, Ix)
% remove across-track stripes in image
% find the 4 corners of the image
[m, n] = size(validDomain);
s = regionprops(validDomain,'centroid');
% sometimes there is more than one region (isloated
% valid pixels) so pick largest region
if length(s) > 1
a = regionprops(validDomain,'area');
idx = max([a.Area]) == [a.Area];
s = s(idx);
clear a idx
end
foo = false([m,n]);
foo(round(s.Centroid(2)),round(s.Centroid(1))) = true;
foo = bwdist(foo);
foo(~validDomain) = 0;
mC = round(m/2);
nC = round(n/2);
% bottom left
[blR, blC] = find((max(max(foo(1:mC,1:nC))) == foo(1:mC,1:nC)),1, 'first');
% bottom right
[brR, brC] = find((max(max(foo(1:mC,(nC+1):end))) == foo(1:mC,(nC+1):end)),1, 'first');
brC = brC+nC;
% top left
[tlR, tlC] = find((max(max(foo((mC+1):end,1:nC))) == foo((mC+1):end,1:nC)),1, 'first');
tlR = tlR + mC;
% top right
[trR, trC] = find((max(max(foo((mC+1):end,(nC+1):end))) == foo((mC+1):end,(nC+1):end)),1, 'first');
trR = trR + mC;
trC = trC + nC;
% figure
% imagesc(validDomain)
% hold on
% plot(blC,blR, 'r*');
% plot(brC,brR, 'g*');
% plot(tlC, tlR, 'b*');
% plot(trC,trR, 'k*');
% plot(s.Centroid(1),s.Centroid(2), 'c*');
% deteremine distance between corner points
corners = [blC, blR; brC, brR; tlC, tlR; trC, trR];
sep = pdist(corners,'euclidean');
if any(sep < mC)
% corners are likely in middle... try diferent
% approach
% bottom left
[blR, blC] = find((max(max(foo(1:(round(mC/2)),1:end))) == foo(1:(round(mC/2)),1:end)), 1, 'first');
% bottom right
[brR, brC] = find((max(max(foo(1:end,round(nC/2*3):end))) == foo(1:end,round(nC/2*3):end)), 1, 'first');
brC = brC+round(nC/2*3)-1;
% top left
[tlR, tlC] = find((max(max(foo(round(mC/2*3):end,1:end))) == foo(round(mC/2*3):end,1:end)),1, 'first');
tlR = tlR + round(mC/2*3) -1;
% top right
[trR, trC] = find((max(max(foo(1:end,1:round(nC/2)))) == foo(1:end,1:round(nC/2))), 1, 'first');
corners = [blC, blR; brC, brR; tlC, tlR; trC, trR];
sep = pdist(corners,'euclidean');
if any(sep < mC)
% at least 2 points are too close to each other
error('two or more recovered image corner locations are too close to eachother\n add to cleanLandsatDataDir corruptImages list then run cleanLandsatDataDir:\n %s'...
, fooFileName)
end
end
clear foo
slope1 = atan((brR - blR) / (brC - blC));
slope2 = atan((trR - tlR) / (trC - tlC));
slope3 = atan((tlR - blR) / (blC - tlC));
slope4 = atan((brR - trR) / (trC - brC));
fooA = false(size(validDomain));
center = round(size(fooA)/2);
fooA(floor(center(1)) - 70 : floor(center(1)) + 70, :) = 1; % create mask - horizontal line
fooA(:,(floor(center(2)) - 100) : (floor(center(2)) + 100)) = 0; % with zeros at the center
% slope will be reduced is image is truncated at corners
% so choose max slope
A = imrotate(fooA,rad2deg(max([slope1,slope2])), 'crop');
B = imrotate(fooA,rad2deg(max([slope3,slope4])), 'crop');
% shift mask to center
shiftCtr = round([s.Centroid(2), s.Centroid(1)]) - center ;
tform = affine2d([1 0 0; 0 1 0; shiftCtr(2) shiftCtr(1) 1]);
[A,~] = imwarp(A,tform);
[B,~] = imwarp(B,tform);
%Perform 2D FFTs
for k = 1:length(size(Ix,3))
fftIm = Ix(:,:,k);
fftIm(fftIm>3) = 3;
fftIm(fftIm<-3) = -3;
fftIm(isnan(fftIm)) = 0;
fftIm = fftshift(fft2(double(fftIm)));
P = abs(fftIm);
mP = mean(P(:));
stdP = std(P(:));
P = (P-mP) > 10*stdP;
sA = sum(P(A));
sB = sum(P(B));
% imagesc(P+A)
if (sA/sB >= 2 || sB/sA >= 2) && (sA > 500 || sB > 500)
if sA > sB
mask = A;
elseif sB > sA
mask = B;
end
%figure; imshow(real(ifft2(ifftshift(fftIm .* (1-(mask ))))));
foo1 = isnan(Ix(:,:,k));
foo2 = real(ifft2(ifftshift(fftIm .* (1-(mask)))));
foo2(foo1) = nan;
%figure; imshow(foo2);caxis([-1.5 1.5])
Ix(:,:,k) = foo2;
clear fftIm mask foo1 foo2
end
end
L4 & 5 imagery is plagued by banding issues that are especially noticeable over low contrast snow and ice surfaces. This causes the feature tracking to lock onto banding discontinuities instead of surface features. To reduce the influence of banding we need to implement an fft filter that deconstructs the image, removes power in the across-track image direction then reforms the image.
Here is a Matlab that removes the banding: