Exemple #1
0
def simulate_coherence(date12_list, baseline_file='bl_list.txt', sensor_name='Env', inc_angle=22.8,
                       decor_time=200.0, coh_resid=0.2, display=False):
    """Simulate coherence for a given set of interferograms
    Inputs:
        date12_list  - list of string in YYMMDD-YYMMDD format, indicating pairs configuration
        baseline_file - string, path of baseline list text file
        sensor_name     - string, SAR sensor name
        inc_angle  - float, incidence angle
        decor_time - float / 2D np.array in size of (1, pixel_num)
                     decorrelation rate in days, time for coherence to drop to 1/e of its initial value
        coh_resid  - float / 2D np.array in size of (1, pixel_num)
                     long-term coherence, minimum attainable coherence value
        display    - bool, display result as matrix or not
    Output:
        cohs       - 2D np.array in size of (ifgram_num, pixel_num)
    Example:
        date12_list = pnet.get_date12_list('ifgram_list.txt')
        cohs = simulate_coherences(date12_list, 'bl_list.txt', sensor_name='Tsx')

    References:
        Zebker, H. A., & Villasenor, J. (1992). Decorrelation in interferometric radar echoes.
            IEEE-TGRS, 30(5), 950-959. 
        Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis
            (Vol. 2). Dordrecht, Netherlands: Kluwer Academic Pub.
        Morishita, Y., & Hanssen, R. F. (2015). Temporal decorrelation in L-, C-, and X-band satellite
            radar interferometry for pasture on drained peat soils. IEEE-TGRS, 53(2), 1096-1104. 
        Parizzi, A., Cong, X., & Eineder, M. (2009). First Results from Multifrequency Interferometry.
            A comparison of different decorrelation time constants at L, C, and X Band. ESA Scientific
            Publications(SP-677), 1-5. 
    """
    date_list, pbase_list, dop_list = read_baseline_file(baseline_file)[0:3]
    tbase_list = ptime.date_list2tbase(date_list)[0]

    # Thermal decorrelation (Zebker and Villasenor, 1992, Eq.4)
    SNR = sensor.signal2noise_ratio(sensor_name)
    coh_thermal = 1. / (1. + 1./SNR)

    pbase_c = critical_perp_baseline(sensor_name, inc_angle)
    bandwidth_az = sensor.azimuth_bandwidth(sensor_name)

    date12_list = ptime.yyyymmdd_date12(date12_list)
    ifgram_num = len(date12_list)

    if isinstance(decor_time, (int, float)):
        pixel_num = 1
        decor_time = float(decor_time)
    else:
        pixel_num = decor_time.shape[1]
    if decor_time == 0.:
        decor_time = 0.01
    cohs = np.zeros((ifgram_num, pixel_num), np.float32)
    for i in range(ifgram_num):
        if display:
            sys.stdout.write('\rinterferogram = %4d/%4d' % (i, ifgram_num))
            sys.stdout.flush()
        m_date, s_date = date12_list[i].split('_')
        m_idx = date_list.index(m_date)
        s_idx = date_list.index(s_date)

        pbase = pbase_list[s_idx] - pbase_list[m_idx]
        tbase = tbase_list[s_idx] - tbase_list[m_idx]

        # Geometric decorrelation (Hanssen, 2001, Eq. 4.4.12)
        coh_geom = (pbase_c - abs(pbase)) / pbase_c
        if coh_geom < 0.:
            coh_geom = 0.

        # Doppler centroid decorrelation (Hanssen, 2001, Eq. 4.4.13)
        if not dop_list:
            coh_dc = 1.
        else:
            coh_dc = calculate_doppler_overlap(dop_list[m_idx],
                                               dop_list[s_idx],
                                               bandwidth_az)
            if coh_dc < 0.:
                coh_dc = 0.

        # Option 1: Temporal decorrelation - exponential delay model (Parizzi et al., 2009; Morishita and Hanssen, 2015)
        coh_temp = np.multiply((coh_thermal - coh_resid), np.exp(-1*abs(tbase)/decor_time)) + coh_resid

        coh = coh_geom * coh_dc * coh_temp
        cohs[i, :] = coh
    #epsilon = 1e-3
    #cohs[cohs < epsilon] = epsilon
    if display:
        print('')

    if display:
        print(('critical perp baseline: %.f m' % pbase_c))
        cohs_mat = coherence_matrix(date12_list, cohs)
        plt.figure()
        plt.imshow(cohs_mat, vmin=0.0, vmax=1.0, cmap='jet')
        plt.xlabel('Image number')
        plt.ylabel('Image number')
        cbar = plt.colorbar()
        cbar.set_label('Coherence')
        plt.title('Coherence matrix')
        plt.show()
    return cohs
Exemple #2
0
def simulate_coherence(date12_list,
                       baseline_file='bl_list.txt',
                       sensor_name='Env',
                       inc_angle=22.8,
                       decor_time=200.0,
                       coh_resid=0.2,
                       display=False):
    """Simulate coherence for a given set of interferograms
    Inputs:
        date12_list  - list of string in YYMMDD-YYMMDD format, indicating pairs configuration
        baseline_file - string, path of baseline list text file
        sensor_name     - string, SAR sensor name
        inc_angle  - float, incidence angle
        decor_time - float / 2D np.array in size of (1, pixel_num)
                     decorrelation rate in days, time for coherence to drop to 1/e of its initial value
        coh_resid  - float / 2D np.array in size of (1, pixel_num)
                     long-term coherence, minimum attainable coherence value
        display    - bool, display result as matrix or not
    Output:
        cohs       - 2D np.array in size of (ifgram_num, pixel_num)
    Example:
        date12_list = pnet.get_date12_list('ifgram_list.txt')
        cohs = simulate_coherences(date12_list, 'bl_list.txt', sensor_name='Tsx')

    References:
        Zebker, H. A., & Villasenor, J. (1992). Decorrelation in interferometric radar echoes.
            IEEE-TGRS, 30(5), 950-959. 
        Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis
            (Vol. 2). Dordrecht, Netherlands: Kluwer Academic Pub.
        Morishita, Y., & Hanssen, R. F. (2015). Temporal decorrelation in L-, C-, and X-band satellite
            radar interferometry for pasture on drained peat soils. IEEE-TGRS, 53(2), 1096-1104. 
        Parizzi, A., Cong, X., & Eineder, M. (2009). First Results from Multifrequency Interferometry.
            A comparison of different decorrelation time constants at L, C, and X Band. ESA Scientific
            Publications(SP-677), 1-5. 
    """
    date_list, pbase_list, dop_list = read_baseline_file(baseline_file)[0:3]
    tbase_list = ptime.date_list2tbase(date_list)[0]

    # Thermal decorrelation (Zebker and Villasenor, 1992, Eq.4)
    SNR = sensor.signal2noise_ratio(sensor_name)
    coh_thermal = 1. / (1. + 1. / SNR)

    pbase_c = critical_perp_baseline(sensor_name, inc_angle)
    bandwidth_az = sensor.azimuth_bandwidth(sensor_name)

    date12_list = ptime.yyyymmdd_date12(date12_list)
    ifgram_num = len(date12_list)

    if isinstance(decor_time, (int, float)):
        pixel_num = 1
        decor_time = float(decor_time)
    else:
        pixel_num = decor_time.shape[1]
    if decor_time == 0.:
        decor_time = 0.01
    cohs = np.zeros((ifgram_num, pixel_num), np.float32)
    for i in range(ifgram_num):
        if display:
            sys.stdout.write('\rinterferogram = %4d/%4d' % (i, ifgram_num))
            sys.stdout.flush()
        m_date, s_date = date12_list[i].split('_')
        m_idx = date_list.index(m_date)
        s_idx = date_list.index(s_date)

        pbase = pbase_list[s_idx] - pbase_list[m_idx]
        tbase = tbase_list[s_idx] - tbase_list[m_idx]

        # Geometric decorrelation (Hanssen, 2001, Eq. 4.4.12)
        coh_geom = (pbase_c - abs(pbase)) / pbase_c
        if coh_geom < 0.:
            coh_geom = 0.

        # Doppler centroid decorrelation (Hanssen, 2001, Eq. 4.4.13)
        if not dop_list:
            coh_dc = 1.
        else:
            coh_dc = calculate_doppler_overlap(dop_list[m_idx],
                                               dop_list[s_idx], bandwidth_az)
            if coh_dc < 0.:
                coh_dc = 0.

        # Option 1: Temporal decorrelation - exponential delay model (Parizzi et al., 2009; Morishita and Hanssen, 2015)
        coh_temp = np.multiply(
            (coh_thermal - coh_resid), np.exp(
                -1 * abs(tbase) / decor_time)) + coh_resid

        coh = coh_geom * coh_dc * coh_temp
        cohs[i, :] = coh
    #epsilon = 1e-3
    #cohs[cohs < epsilon] = epsilon
    if display:
        print('')

    if display:
        print(('critical perp baseline: %.f m' % pbase_c))
        cohs_mat = coherence_matrix(date12_list, cohs)
        plt.figure()
        plt.imshow(cohs_mat, vmin=0.0, vmax=1.0, cmap='jet')
        plt.xlabel('Image number')
        plt.ylabel('Image number')
        cbar = plt.colorbar()
        cbar.set_label('Coherence')
        plt.title('Coherence matrix')
        plt.show()
    return cohs