Instabilities of the antenna beam orientation lead to radiometric errors in SAR images. This is one of the most difficult problems for the application of the range-Doppler algorithm, which implies a straight-line flight with a constant orientation. In the paper, we propose a radiometric correction approach for compensation of such instabilities.
Author: admin
Multi-Look Stripmap SAR Processing Algorithm with Built-In Correction of Geometric Distortions
SAR systems installed on small aircrafts suffer from trajectory deviations and instabilities of antenna orientation. These kinds of motion errors lead to significant geometric distortions in SAR images. In the paper, we describe a time-domain multi-look stripmap SAR processing algorithm with built-in correction of geometric distortions. In the algorithm, the azimuth reference functions and range migration curves are specially designed to produce SAR images directly on a correct rectangular grid on the ground plane. The proposed technique has been successfully tested by using a Ku-band airborne SAR system.
Correction of Radiometric Errors by Multi-Look Processing with Extended Number of Looks.
The application of the clutter-lock technique in case of fast and significant instabilities of the antenna orientation leads to strong geometric distortions in SAR images. In the paper we propose a radiometric correction approach which could be used with SAR processing algorithms without clutter-lock.
Multi-Look SAR Processing with Build-In Geometric Correction
Synthetic aperture radar (SAR) systems onboard small aircrafts suffer from trajectory deviations and instabilities of antenna orientation. These kinds of motion errors lead to significant geometric distortions in SAR images. In order to correct the distortions, we propose a time-domain multi-look stripmap SAR processing algorithm with built-in geometric correction. In the algorithm, the azimuth reference functions and range migration curves are designed to produce SAR images directly on a correct rectangular grid on the ground plane. The proposed technique has been successfully tested by using a Ku-band airborne SAR system installed onboard light-weight aircraft.
Improving SAR Images: Built-In Geometric and Multi-Look Radiometric Corrections
Abstract—SAR systems installed on small aircrafts and UAVs suffer from trajectory deviations and instabilities of antenna orientation. These kinds of motion errors lead to significant geometric distortions and radiometric errors in SAR images. In the paper, we describe a time-domain multi-look stripmap SAR processing algorithm with built-in geometric and multi-look radiometric corrections. Geometric correction is performed due to azimuth reference functions and range migration curves specially designed to produce SAR images directly on a rectangular grid on the ground plane. Radiometric correction is based on multi-look processing with extended number of looks. The proposed techniques have been successfully tested with a Ku-band SAR system installed on a light-weight aircraft.
Multi-Look SAR Processing with Built-In Geometric Correction
In the paper a time-domain multi-look stripmap SAR processing algorithm with built-in geometric correction is considered. In the algorithm, the azimuth reference functions and range migration curves are designed to produce SAR images directly on a correct rectangular grid on the ground plane.
Correction of Radiometric Errors by Multi-Look Processing with Extended Number of Looks
The application of the clutter-lock technique in case of fast and significant instabilities of the antenna orientation leads to strong geometric distortions in SAR images. In the paper we propose a radiometric correction approach which could be used with SAR processing algorithms without clutter-lock.
Tesseract Library Configuration
Tutorial for Installing Tesseract
You’ve undoubtedly seen it before… It’s widely used to process everything from scanned documents to the handwritten scribbles on your tablet PC and Google Translate. And today you’ll create your first app for text recognition.
What is OCR?
Optical Character Recognition, or OCR, is the process of electronically extracting text from images and reusing it in a variety of ways such as document editing, free-text searches, or compression. In this tutorial, you’ll learn how to install Tesseract, an open-source OCR engine maintained by Google.
How to Install Tesseract for Microsoft Visual Studio?
Step 1:
To install Tesseract you need to install the following programs:
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https://git-scm.com/ |
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https://www.sliksvn.com/en/download |
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https://www.visualstudio.com |
Step 2:
What’s next? That’s right, create a folder where we want to install Tesseract. This can be any directory on your computer, for example: “D:\Tesseract-files”.
After that, run GIT CMD and move to Tesseract`s folder. Your GIT command line should look like this:

Fig. 1. GIT CMD example
Step 3:
Now you need to copy the entire dependency from the GitHub repository to your computer. To do this, we write the following command in GIT CMD:
git clone git://github.com/pvorb/tesseract-vs2013.git. In the console GIT CMD you will see something like this:

Fig. 2. Clone tesseract-vs2013.git
After executing this command, you will see the following in the console:

Fig. 3. Clone tesseract-vs2013 done
Step 4:
For the next step, run VS2013 developer command Prompt. It is in: {directory of MS VS}\Common7\Tools\Shortcuts\Developer Command Promt VS2013. And move to D:\Tesseract-files\tesseract-vs2013.

Fig. 4. Command promt for VS2013
Now we can perform building using the command msbuild build.proj:

Fig. 5. Start performing build
After this step, the VS2013 can be closed.
Step 5:
Reopen GIT CMD and check folder and check the working directory. Must be “D:\Tesseract-files\”. After that, gets the latest source using SVN (print in GIT CMD): svn checkout https://github.com/svn2github/Tesseract.git.

Fig. 6. Checkout Tesseract
After performing this procedure, the new folder appears in a folder D:\Tesseract-files\ which name is Tesseract.git\.
Move in GIT CMD to D:\Tesseract-files\Tesseract.git\trunk and apply the patch provided in tesseract-vs2013 (print in cmd): svn patch D:\Tesseract-files\tesseract-vs2013\vs2013+64bit_support.patch

Fig. 7. Patch provided in tesseract-vs2013
Copy both directory (lib and include) from D:\Tesseract-files\tesseract-vs2013\release into D:\Tesseract-files\Tesseract.git\trunk\
Open D:\Tesseract-files\Tesseract.git\trunk\vs2013\tesseract.sln with Visual Studio 2013.
Step 6:
Open Property pages of libtesseract304 and in Configuration Properties->C/C++->General->Additional Include Directories add D:\Tesseract-files\Tesseract.git\trunk\include\ and D:\Tesseract-files\Tesseract.git\trunk\include\ leptonica\; In Property pages open Linker->General->Additional Library Directories add D:\Tesseract-files\Tesseract.git\trunk\lib\x64\;
It is necessary to repeat this operation for Debug and Release. Build the project in Release and Debug.
Step 7:
What would Tesseract recognized the text he needs training files. They can be found in: https://github.com/tesseract-ocr/tessdata. Download the necessary files and copy them to D: \Tesseract-files\Tesseract.git\trunk\ tessdata\
Step 8:
Copy tesseract`s .dll files to necessary project from D:\Tesseract-files\Tesseract.git\lib copy libtesseract304.dll (or libtesseract304d.dll) to Release (or Debug) folder in necessary project (In this folder must be exe file).From D:\Tesseract-files\tesseract-vs2013\lib\x64 (or X64) copy liblept171.dll (or liblept171d.dll) to Release (or Debug) folder in necessary project (In this folder must be exe file).
Connect Tesseract into project (is necessary for Debug and for Release).
Set properties of necessary project:
in C/C++ –> General –> Additional Include Directories:
D:\Tesseract-files\Tesseract.git\trunk\
D:\Tesseract-files\Tesseract.git\trunk\ccmain
D:\Tesseract-files\Tesseract.git\trunk\ccstruct
D:\Tesseract-files\Tesseract.git\trunk\ccutil
D:\Tesseract-files\Tesseract.git\trunk\leptonica
D:\Tesseract-files\Tesseract.git\trunk\api
D:\Tesseract-files\Tesseract.git\trunk\include
In Linker –> General –> Additional Library Directories:
D:\Tesseract-files\Tesseract.git\lib\x64
D:\Tesseract-files\Tesseract.git\lib\
In Linker –> Input –> Additional Dependencies:
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libtesseract304d.lib |
for Release
libtesseract304d.lib |
Step 9:
So, create new console application and paste this code:
#include “baseapi.h”
#include “allheaders.h”
int main()
{
char *outText;
tesseract::TessBaseAPI *api = new tesseract::TessBaseAPI();
// Initialize tesseract-ocr with English, without specifying tessdata path
if (api->Init(“D:\\Tesseract-files\\Tesseract.git\\trunk”, “eng”)){
fprintf(stderr, “Could not initialize tesseract.\n”);
exit(1);
}
// Open input image
Pix *image = pixRead(“yout_image.tif”);
api->SetImage(image);
// set list of allowed characters
api->SetVariable(“tessedit_char_whitelist”, “abcdefghijklmnoprstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ-.;,:/0123456789”);
// Get OCR result
outText = api->GetUTF8Text();
printf(“OCR output:\n%s”, outText);
// Destroy used object and release memory
api->End();
delete[] outText;
pixDestroy(&image);
return 0;
}
Then build and compile the project.
As a result, you will get:

Fig.8. Input image

Fig. 9. Output result
Congratulation! You installed and started your first text recognition program!


