Sentinel Interferometric Wide Focusing #3
Comments
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Dear Luca Manca, Thank you, |
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Hi, Best regards, |
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The following is the image plot of the Binary file i-e resultSW11_T1286748902_NQ12061.bin (unfocused as i understand)
I use the following code to plot 'sol(:,k)' as image after range compression to check for some range compression data % range compression completed I get the following results.
I am not sure if it is being compressed properly, because the display of my results vary alot from yours or jmfried`s. (Please guide if there is any other way) i get the following result after imagesc function is applied to ssol(:,k) at the end of the m file,
This result does not explain anything as far as i understand. in the end i apply the imagesc function to the X-matrix obtained after running the Read_bin.m file.
The issue I have is, I believe i am not getting the desired results as can be seen from the plots. the data is of a region over Manaus, near Brazil which comprises of both Rivers and Land. Thank you for your reply, |
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Jmfriedt: Right, can you guide me as to how can i get good results, and display range compressed data and the final image properly, in the range compression phase i was trying to plot the range compressed data which i failed to do so as can be seen. |
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Please provide the reference to the exact RAW dataset you are investigating (the filename on the Copernicus repository) so I can download and reproduce your processing. |
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This is the data set that I have been using, S1B_IW_RAW__0SDV_20201014T221423_20201014T221455_023814_02D411_C1D3.SAFE if it is offline in the copernicus respository, it can be accessed from here, (Alaska satellite Facility) Thank you, Best Regards |
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I processed the same dataset and indeed abs(x(1:10:end,:)) after x=read_bin('resultSW11_T1286748902_NQ12061.bin',2000,12061*2); does not show much feature. However I had a look at the (more recent) S1B_IW_SLC__1SDV_20210729T221428_20210729T221456_028014_03578E_A6B0
over the same area and unless you select a dataset on the southwestern lake, you might not be able to see much feature on the homogeneous Amazon forest. Sao Paulo has been selected as a reference dataset because it features StripMap (SM) measurements (as opposed to Interferometric Wide (IW) -- easier for beginning) and point-like ships waiting in the harbour (actually I have been told that the harbour is not called Sao Paulo but Santos), so unless you have a specific reason for analyzing the raw data of a forest covered area, I would consider at first areas with easier to identify features. Maybe you will find it more satisfactory to run x=read_bin('resultSW10_T1286748884_NQ11924.bin',1400,11924*2);
imagesc((abs(x(1:10:end,:))),[0 1000])which shows some of the features on the south-western part of the image (I stopped
I'll try to finish the azimuth compression as mentioned to LucaMancaITA by using the theoretical parabolic shape of the phase along azimuth to see if the image can be completely focused in both directions. Best, JM |
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I tried function [sol,ssol,mychirp]=read_bin(filename, count, l)
% file name, number of complex data to read, line length
% x=read_bin('resultSW10_T1286748881_NQ11924.bin',2000,11924*2);
% imagesc(abs(x(1:10:end,:)));
pkg load signal
f=fopen(filename,'rb');
if (f<0) v=0;
else
fref=37.53472224
% UPDATE THESE VALUES: here taken from S1A_IW_RAW__0SDV_20210410T055133_20210410T055205_037384_0467D7_5D9B
IW=str2num(filename(9:10)) % swath number
if (IW==10)
fs=3/7*4*fref % case 10 in Range Decimation, p.35 of Packet Protocol Data Unit
TXPRRcode=1605; % upchirp
TXPSFcode=-12335; % start
TXPLcode=1967;
PRI=21859
else if (IW==11)
fs=4/11*4*fref % case 11 in Range Decimation, p.35 of Packet Protocol Data Unit
TXPRRcode=1160; % upchirp
TXPSFcode=-10546; % start
TXPLcode=2327;
PRI=25857
else if (IW==12)
fs=5/16*4*fref % case 12 in Range Decimation, p.35 of Packet Protocol Data Unit
TXPRRcode=1193; % upchirp
TXPSFcode=-9341; % start
TXPLcode=2004;
PRI=22265
else printf("Wrong swath number");
end
end
end
% range compression
TXPRR=TXPRRcode*fref^2/2^21 % MHz/us
TXPSF=TXPRR/4/fref+TXPSFcode/2^14*fref % MHz
TXPL=TXPLcode/fref % us
N=TXPL*fs
tim=linspace(-TXPL/2,TXPL/2,N);
phi1=TXPSF+TXPRR*TXPL/2
phi2=TXPRR/2
nomchip=exp(j*2*pi*(phi1*tim+phi2*tim.^2));
sol=zeros(l,count); % size(t)(2),size(t)(1)); % avoid dynamic memory allocation
for k=1:count % size(t)(1) % 6592 = sweep along azimuth for range compression
t=fread(f,2*l,'float'); % dynamically read to avoid filling memory with a huge t
t=t(1:2:end)+t(2:2:end)*i; % see GNU Radio's read_complex_binary
k
tmp=xcorr(nomchip,t);
tmp=tmp(floor(length(nomchip)/2)+1:length(t)+floor(length(nomchip)/2));
sol(:,k)=tmp;
end
% range compression completed
clear t
fclose(f)
%
%https://celestrak.com/satcat/tle.php?CATNR=39634
%SENTINEL-1A
%1 39634U 14016A 21079.09896275 -.00000050 00000-0 -79699-6 0 9995
%2 39634 98.1819 88.1319 0001317 80.0185 280.1166 14.59198711370760
% https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/products-algorithms/level-1/single-look-complex/stripmap
% range resolutio = 3.6 m
% NQ=9975*2*3.1 => ans = 61845 m @ 43.8 mean angle
PRF=fref*1e6/PRI
t=[0:800]/PRF; % time TODO ADJUST
t=t-t(floor(length(t)/2));
l=300/5405; % lambda wavelength
REarth=6371 % km
Hleo=693 % km
Hgeo=35786 % km
period=sqrt((Hleo+REarth)^3/(Hgeo+REarth)^3)*24*3600 % s
orbit=2*pi*(Hleo+REarth)*1000 % m
% TLE: 24/14.5919871*3600=5921.1 s !
Vs=orbit/period % m/s
Ve=Vs/sqrt(1+Hleo/REarth) % m/s
for th=45 % [42.05 45.49 43.8]
theta=th*pi/180; % S6 angle (rad)
R0=Hleo/cos(theta)*1000; % m
Rpp=Ve^2/R0 % m.s^-2
-4*pi/l*Rpp*(t(2)-t(1))
mychirp=-4*pi/l*Rpp/2*t.^2;mychirp=exp(j*mychirp); % phase evolution of azimuth chirp
figure
plot(t,mychirp)
% azimuth compression
ssol=sol'; % avoid dynamic memory allocation
for k=1:l % size(sol)(1) % 19950 = sweep range for azimuth compression
k
tmp=xcorr(mychirp,sol(k,:));
tmp=tmp(floor(length(mychirp)/2)+1:size(sol)(2)+floor(length(mychirp)/2));
ssol(:,k)=tmp;
end
end
endand running with |
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i implemented the code as you guided uptil range compression, I was successful in my amazon dataset, I then also implemented it on your Sao Polo data set, as you guided that water resources can help beginners , I got the following results
for this dataset, resultSW11_T1297499458_NQ11995.bin which I think is pretty good, as it does show, that some features are present. Thank you so much for your guidance, I`ll now try to move on to azimuth compression, looking forward for your guidance again. Thank You, |
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How are you calculating TXPRRcode, TXPLcode and TXPSFcode for different modes? Thank you, |
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See Sentinel-1-SAR-Space-Packet-Protocol-Data-Unit.pdf in the doc/ directory, pp.37-39, with the TXPRRcode, TXPLcode and TXPSFcode values extracted from the raw level0 files telemetry. These values are given on the console output of |
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How do you Plot the uncompressed data? f=fopen(resultSW11_T1297499460_NQ11995.bin,'rb'); I just get a solid colored blue result. More-so, how were you able to plot the SaoPolo_SM_Level 0 image with such good features, Thank you, |
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I have updated the script as I computed the phase but forgot to create a complex azimuth chirp exp(j*phase). The result is not yet good though. When you imagesc() you must understand that the last argument [0 1000] is the color scale range: leave it empty at first unless you want to zoom on some feature in the color range by for e.g. saturating a strong target. |
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Hi jmfriedt, As first approximation I set the doppler centroid frequency to zero. With this I was able to obtain this SLC product (which still it's a bit blurry if compared to the one obtained by using the chirp found in the range-compressed image) Hope this can be helpful, |
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Hi Luca, Thank you, |
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I updated https://github.com/jmfriedt/sentinel1_level0/tree/main/saopaolo_SM with the script for range compression, extracting the azimuth chirp from the strongest target and azimuth compression. Will try now with this expression of the azimuth chirp. Thanks for the contribution. |
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I don't know what I did wrong previously, I suspect using exp(phi) instead of exp(j*phi) when writing the phase expression. I have updated https://github.com/jmfriedt/sentinel1_level0/tree/main/saopaolo_SM including pulse_compression.m and the resulting azimuth compressed images in the associated README.md file. Unfortunately I cannot commit all1500bot.mat which is 1.5 GB and even compressed larger than 1 GB: it is the subset of the Sao Paulo Stripmap IQ stream reorganized as a matrix according to the Pulse Repetition Interval, whos on the sol variable: |
Dear Jimfriedt,
first of all I would like to thank you again for the amazing work you provided.
After succesfully decoded a raw Sentinel IW image, I am trying to focus it. For what concern the range compression I am using the formula available in your code (in accordance with the Level 1 Detailed Algorithm Definition document) and the result seems to be something similar to what one can expect after a range compression (see figure below).
However I still have issues in the azimuth compression process. My question is: why you did not used the following azimuth chirp formula used in the Range Doppler Algorithm?
C(s) = exp( j * (4* pi / lambda) * R(s)), approximating R(s) using a Taylor expansion
Below the images of the decoded raw IW Sentinel image (swath 2), the image after range compression (still not understand why it is mirrored along the y axis) and the corresponding slc image (read using snappy toolbox)
My other question is: is there a way to focus raw Sentinel SM/IW images without creating the reference azimuth chirp doing a crude search for the strongest target in the scene?
Thanks again for your time,
Luca
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