Ultra-resolution method based on polarization synthetic aperture radar image
A synthetic aperture radar, super-resolution technology, applied in the direction of reflection/re-radiation of radio waves, utilization of re-radiation, measurement devices, etc., can solve problems such as the inability to retain phase information and the fully polarized scattering characteristics of scatterers
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specific Embodiment approach 1
[0043] Specific implementation mode one: combine figure 1 Describe this embodiment mode, the method described in this embodiment mode is realized by the following steps;
[0044] Step 1: Read in the full polarization SAR image data according to the data format;
[0045] Step 2: Preprocessing the read full polarization SAR image data, and using the polarization target decomposition method to obtain different scattering components;
[0046] Step 3: Divide each pixel of the original low-resolution image of each scattering component into 2×2 sub-pixels on average to form an initial high-resolution image;
[0047] Step 4: Determine the processing window in the high-resolution image, use the polarization spatial correlation to obtain the proportion of each sub-pixel in the processing window, and then obtain the nth super-resolution image of each scattering component;
[0048] Step 5: When n=1, calculate the root mean square error between the super-resolution image and the initial ...
specific Embodiment approach 2
[0051] Embodiment 2: The difference between this embodiment and Embodiment 1 is that the polarization target decomposition method in step 2 adopts the Pauli decomposition method; Pauli decomposition is the most classic coherent target decomposition method. In the case of reciprocity, the scattering matrix Decomposed into odd scatter, even scatter, and even scatter with a 45-degree inclination to the horizontal direction,
[0052] [ S ] = s hh s hv s vh s vv α = [ S ] a + ...
specific Embodiment approach 3
[0054] Specific implementation mode three: combination Figure 2 to Figure 5 This embodiment is described. The difference between this embodiment and the specific embodiment 1 is that in step 4, the strong spatial correlation of polarization of adjacent pixels is used to obtain the proportion of each sub-pixel in the processing window, and obtain a super-resolution image.
[0055] image 3 is the original low-resolution image, after super-resolution processing, each low-resolution pixel (such as A 5 ) is divided into four high-resolution sub-pixels (such as A 51 , A 52 , A 53 , A 54 ), you can get Figure 4 High resolution images shown.
[0056] Take sub-pixel A 51 and its 3×3 neighborhood, such as Figure 4 As shown in the thick solid line box in , to define sub-pixel A 51 The spatial correlation coefficient of is:
[0057] R 51 =|α 51 -α 14 | 2 +|α 51 -α 23 | 2 +|α 51 -α 24 | 2 +|α 51 -α 12 | 2 (1)
[0058] +|α 51 -α 52 | 2 +|α 51 -α 44 | 2 ...
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