METHOD AND SYSTEM FOR FORMING VERY LOW NOISE IMAGERY USING PIXEL CLASSIFICATION
First Claim
1. A method for generating images from projection data comprising:
- inputting from at least one data receiving element first values representing correlated positional and recorded data;
each of said first values forming a point in an array of N data points;
forming an image by processing the projection data utilizing a pixel characterization imaging subsystem that combines the positional and recorded data to form the SAR imagery utilizing one of a back-projection algorithm or range migration algorithm;
integrating positional and recorded data from many aperture positions, comprising;
forming the complete aperture A0 for SAR image formation comprising collecting the return radar data, the coordinates of the receiver, and the coordinates of the transmitter for each position k along the aperture of N positions;
forming an imaging grid comprising M image pixels wherein each pixel Pi in the imaging grid is located at coordinate (xP(i),yP(i), zP(i));
selecting and removing a substantial number of aperture positions to form a sparse aperture Ai;
repeating the selecting and removing step for L iterations for each Ai;
classifying each pixel in the image into either target class based on the statistical distribution of its amplitude across L iterations (1≦
i≦
L);
whereby if an image pixel is classified so as to be associated with a physical object, its value is computed from its statistics;
otherwise, the pixel is assumed to come from a non-physical object and is given the value of zero.
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Abstract
A method and system for generating images from projection data comprising inputting from at least one data receiving element first values representing correlated positional and recorded data; each of said first values forming a point in an array of k data points; forming an image by processing the projection data utilizing a pixel characterization imaging subsystem that combines the positional and recorded data to form the SAR imagery utilizing one of a back-projection algorithm or range migration algorithm; integrating positional and recorded data from many aperture positions, comprising: forming the complete aperture A0 for SAR image formation comprising collecting the return radar data, the coordinates of the receiver, and the coordinates of the transmitter for each position k along the aperture of N positions; forming an imaging grid comprising M image pixels wherein each pixel Pi in the imaging grid is located at coordinate (xP(i),yP(i), zP(i)); selecting and removing a substantial number of aperture positions to form a sparse aperture Ai; repeating the selecting and removing step for L iterations for each Ai; classifying each pixel in the image into either target class based on the statistical distribution of its amplitude across L iterations (1≦i≦L); whereby if an image pixel is classified so as to be associated with a physical object, its value is computed from its statistics; otherwise, the pixel is assumed to come from a non-physical object and is given the value of zero.
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Citations
20 Claims
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1. A method for generating images from projection data comprising:
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inputting from at least one data receiving element first values representing correlated positional and recorded data;
each of said first values forming a point in an array of N data points;forming an image by processing the projection data utilizing a pixel characterization imaging subsystem that combines the positional and recorded data to form the SAR imagery utilizing one of a back-projection algorithm or range migration algorithm; integrating positional and recorded data from many aperture positions, comprising; forming the complete aperture A0 for SAR image formation comprising collecting the return radar data, the coordinates of the receiver, and the coordinates of the transmitter for each position k along the aperture of N positions; forming an imaging grid comprising M image pixels wherein each pixel Pi in the imaging grid is located at coordinate (xP(i),yP(i), zP(i)); selecting and removing a substantial number of aperture positions to form a sparse aperture Ai; repeating the selecting and removing step for L iterations for each Ai; classifying each pixel in the image into either target class based on the statistical distribution of its amplitude across L iterations (1≦
i≦
L);
whereby if an image pixel is classified so as to be associated with a physical object, its value is computed from its statistics;
otherwise, the pixel is assumed to come from a non-physical object and is given the value of zero. - View Dependent Claims (2, 3, 4, 5, 6, 7, 9)
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8. A method for generating images from projection data comprising:
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a) inputting data from a scanned area having first values representing correlated positional and recorded data;
each of said first values forming a point in an array of k data points;b) forming an aperture A0 consisting of N elements, each element comprising radar receiving position information (xR(k),yR(k), zR(k)) 1≦
k≦
N, the radar transmitting information (xT(k),yT(k), zT(k)), and the data record sk(t) that was measured at the location;c) forming the imaging grid comprising M image pixels wherein each pixel Pi in the imaging grid is located at coordinate (xP(i),yP(i), zP(i)) using a backprojection or range migration algorithm; d) generating the backprojection value a jth pixel computed by using the equation - View Dependent Claims (10, 11, 12, 13, 14)
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15. A system for generating images from projection data comprising:
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at least one processor for processing image information;
the at least one processor having an input for inputting scanned data having first values representing correlated positional and recorded data;
each of said first values forming a point in an array of N data points;the at least one processor operating to perform the following steps; (h) forming an aperture A0 consisting of N elements, each element comprising radar receiving position information (xR(k),yR(k), zR(k)) 1≦
k≦
N, radar transmitting information (xT(k),yT(k), zT(k)), and the data record sk(t) that was measured at the location;(i) forming the imaging grid comprising M image pixels wherein each pixel Pi in the imaging grid is located at coordinate (xP(i),yP(i), zP(i)) using one of backprojection, range migration algorithm, or polar format; (j) generating the value a jth pixel computed by
P0j=F(w0k,k,k,j) where 1≦
k≦
N and 1≦
j≦
Mand the baseline image I0=P0j; (k) assigning the value of weighting factors w0k to be 1 for A0, w0k defining which aperture positions contribute to the formed image or do not contribute; (l) generating a sparse aperture Ai having K positions from the complete aperture A0 having N positions where Ai, 1≦
i≦
L;
where L is the number of iterations, using the equation
Pij=F(wik,k,j) where 1≦
k≦
N and 1≦
j≦
Mto form the image from the sparse apertures Ai, and where the value of wik is either 0 or 1 to define which aperture positions contribute to the formed image, and where there are K elements of wik having the value of 1, and (N−
K) elements of wik having the value of 0;
which (N−
K) and K locations inside the array Ai of are randomized for each iteration;(m) forming the image Ii using data from the sparse aperture Ai where the backprojection value of the jth pixel using the sparse aperture A is found by computing the magnitude Ei using
Ei=|Hilbert(Ii)|,where Ii is defined as Ii=Pij; (n) repeating the steps of (e) and (f) for L iterations; whereby the value of each pixel is examined across L iterations to make a decision and classify whether or not the pixel belongs to a physical object to thereby remove unwanted noise in the generation of an image using electromagnetic signals. - View Dependent Claims (16, 17, 18, 19, 20)
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20. The system of claim 17 wherein the step of generating the value a jth pixel is computed by using the equation
Specification