Compute the minimum distance between each element in a vector with another larger array

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I have an array say, MW that contains the latitudes and longitudes of ~1300 pixels. I also have a reference text file, say IR that contains an enormously large number of pixels with latitudes and longitudes and the pixels here are associated with unique IDs. My goal is to attribute each MW pixel to a unique ID found in the IR text file if possible. I have a function called distance_latlon (function d = distance_latlon(lat1,lat2,lon1,lon2)) that computes the distances between any two points given their lat and lon. Essentially, I would like to compute for each MW pixel, the distance to all of the IR pixels and find the IR pixel within the closest distance and attribute the ID. If the MW pixel does not have a single IR pixel within a certain distance threshold, then I can simply assign a 0.
How do I accomplish this task without the extremely expensive way that is, going through each MW pixel in a for loop, running the distance function between each MW pixel and all the IR pixels in the text file in another long for loop and finding the minimum distance? What is the least expensive way of doing this?
  2 个评论
Bruno Luong
Bruno Luong 2020-8-19
编辑:Bruno Luong 2020-8-19
What is the size of IR (something more precise than "enormously large number")? If you provide the size of MW, it would be nice if you provide the size of IR as well.
What returns distance_latlon(lat1,lat2,lon1,lon2)? Is it the geodesic distance on sphere?
Sai Prasanth
Sai Prasanth 2020-8-20
It was initially of the size of 100s of millions (but I was able to subset it and bring it down to 76,000). And yes, it is the distance between two points given their lat/lon on a sphere (Heaversine formula) described here: https://www.geeksforgeeks.org/program-distance-two-points-earth/

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Rik
Rik 2020-8-19
I don't think there is a way around doing some calculation for each pair.
Is there a way to vectorize distance_latlon? If not, I would suggest a pre-selection, so you only need to do the expensive calculation a few times:
N_small=5;
[lat2,lon2]=MyFun(IR);
for n_MW=1:1300
[lat1,lon1]=MyFun(MW(n_MW));
quickdist=hypot(lat1-lat2,lon1-lon2);
[~,idx1]=sort(quickdist);
lat2_small=lat2(idx(1:N_small));
lon2_small=lon2(idx(1:N_small));
for n_small=1:N_small
exactdist(n_small)=distance_latlon(lat1,lat2_small,lon1,lon1_small);
end
[min_exactdist,idx2]=min(exactdist);
rowID=idx1(idx2);
end
  6 个评论
Rik
Rik 2020-8-21
Sure, the part with quickdist was based on the assumption that running it directly was too slow.
That function does seem to support vector input, so I would suggest you try it to see if it suits your need.
Sai Prasanth
Sai Prasanth 2020-8-21
After you pointed me towards thinking about vector inputs, I think using the combination of Matlab's distance and rangeselect functions are the quickest way to get this done.
a = -90;
b = 90;
rand_lat1 = (b-a).*rand(1300,1) + a;
rand_lat2 = (b-a).*rand(76000,1) + a;
a = -180;
b = 180;
rand_lon1 = (b-a).*rand(1300,1) + a;
rand_lon2 = (b-a).*rand(76000,1) + a;
MWpixels = [rand_lat1 rand_lon1]; % Rep. the MW pixels
IRpixels = [rand_lat2 rand_lon2]; % Rep. the IR pixels
% Objective: To find the IR pixels that are close to the MW pixel.
tic
% rangesearch(X,Y,r): Finds all the X points that are within distance r of the Y points
[Idx,D] = rangesearch(IRpixels,MWpixels,1000,'Distance',@distfun);
toc
%%%%%%%%%% Distance Function %%%%%%%%%%%
function D = distfun(vec_latlon_1,vec_latlon_2)
% The first column represents latitudes and the second column represents
% longitudes for both inputs
rad_Earth = 6371; % In km
D = distance(vec_latlon_1,vec_latlon_2,[rad_Earth 0]);
end

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