63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
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import numpy as np
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from tool.algorithm.image.ImageHandle import ImageHandler
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from matplotlib import pyplot as plt
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import matplotlib as mpl
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from scipy import signal
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import scipy
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cmap = mpl.cm.jet_r
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def show_filt():
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mas = r"D:\micro\LWork\VegetationHeight\Temporary\preprocessed\master_s2\s11.bin"
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aux = r"D:\micro\LWork\VegetationHeight\Temporary\preprocessed\slave_s2\s11.bin"
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out_file = r'D:\micro\LWork\VegetationHeight\Temporary\preprocessed\test_def.tif'
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mas_arr = np.fromfile(mas, np.complex64)
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aux_arr = np.fromfile(aux, np.complex64)
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# mas_new = mas_arr.reshape(1307, 1900)
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# aux_new = aux_arr.reshape(1307, 1900)
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mas_new = mas_arr.reshape(6419, 5117)
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aux_new = aux_arr.reshape(6419, 5117)
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# mas_arr = ImageHandler.get_all_band_array(mas)
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# aux_arr = ImageHandler.get_all_band_array(aux)
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# mas_new = mas_arr[:,:,0] + mas_arr[:,:,1] * 1j
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# aux_new = aux_arr[:,:,0] + aux_arr[:,:,1] * 1j
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res = mas_new * np.conj(aux_new)
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ang1 = np.angle(res, deg=False)
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ImageHandler.write_img(out_file, '', [0, 0, 0, 0, 0, 0], ang1)
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plt.imshow(ang1, cmap="hsv")
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plt.colorbar()
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plt.set_cmap(cmap)
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plt.show()
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def compute_flat_phase_estimate(sar_image):
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# 假设sar_image是输入的SAR图像数据，是一个二维的复数数组，其中每个元素包含振幅和相位信息
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# 首先，计算SAR图像的幅度
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sar_amplitude = np.abs(sar_image)
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# 对幅度进行多普勒校正，这个步骤通常包括对图像进行配准和校正
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# 这里仅仅做示例，实际情况下你可能需要更复杂的配准和校正步骤
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sar_amplitude_corrected = sar_amplitude
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# 计算多普勒校正后的SAR图像的相位
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sar_phase = np.angle(sar_image)
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# 使用信号处理中的方法（例如2D FFT）估计平地相位
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# 在这个示例中，我们使用3x3的中值滤波器来平滑相位图像
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flat_phase_estimate = signal.medfilt(sar_phase, kernel_size=3)
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plt.imshow(flat_phase_estimate[:,:,1], cmap="hsv")
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plt.colorbar()
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plt.set_cmap(cmap)
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plt.show()
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return flat_phase_estimate
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if __name__ == '__main__':
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show_filt()
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