def get_rma():
# type: () -> RMAType
img_geometry = img['image_geometry']
near_range = img_geometry['range_to_first_sample']
center_time = parse_timestring(img['center_pixel']['center_time'],
precision='us')
first_time = parse_timestring(img_geometry['first_line_time'],
precision='us')
zd_time_scp = get_seconds(center_time, first_time, 'us')
r_ca_scp = near_range + image_data.SCPPixel.Row * grid.Row.SS
time_ca_poly = numpy.array(
[zd_time_scp, -look * ss_zd_s / grid.Col.SS], dtype='float64')
timecoa_value = get_seconds(center_time, start_time)
arp_velocity = position.ARPPoly.derivative_eval(timecoa_value,
der_order=1)
vm_ca = numpy.linalg.norm(arp_velocity)
inca = INCAType(R_CA_SCP=r_ca_scp,
FreqZero=fc,
TimeCAPoly=time_ca_poly,
DRateSFPoly=[
[1 / (vm_ca * ss_zd_s / grid.Col.SS)],
])
return RMAType(RMAlgoType='RG_DOP', INCA=inca)
def get_rma() -> RMAType:
dop_centroid_poly = Poly2DType(Coefs=dop_centroid_poly_coeffs)
dop_centroid_coa = True
if collect_info.RadarMode.ModeType == 'SPOTLIGHT':
dop_centroid_poly = None
dop_centroid_coa = None
# NB: DRateSFPoly is defined as a function of only range - reshape appropriately
inca = INCAType(
R_CA_SCP=r_ca_scp,
FreqZero=center_freq,
DRateSFPoly=Poly2DType(
Coefs=numpy.reshape(drate_sf_poly_coefs, (-1, 1))),
DopCentroidPoly=dop_centroid_poly,
DopCentroidCOA=dop_centroid_coa,
TimeCAPoly=Poly1DType(Coefs=time_ca_poly_coeffs))
return RMAType(RMAlgoType='OMEGA_K', INCA=inca)
def get_rma():
# type: () -> RMAType
inca = INCAType(FreqZero=center_frequency)
return RMAType(RMAlgoType='OMEGA_K',
INCA=inca)
def get_rma():
# type: () -> RMAType
return RMAType(RMAlgoType='OMEGA_K', INCA=INCAType(DopCentroidCOA=True))
def _get_rma_adjust_grid(self, scpcoa, grid, image_data, position,
collection_info):
"""
Gets the RMA metadata, and adjust the Grid.Col metadata.
Parameters
----------
scpcoa : SCPCOAType
grid : GridType
image_data : ImageDataType
position : PositionType
collection_info : CollectionInfoType
Returns
-------
RMAType
"""
look = scpcoa.look
start_time = self._get_start_time()
center_freq = self._get_center_frequency()
doppler_bandwidth = float(
self._find('./imageGenerationParameters'
'/sarProcessingInformation'
'/totalProcessedAzimuthBandwidth').text)
zero_dop_last_line = parse_timestring(
self._find('./imageGenerationParameters'
'/sarProcessingInformation'
'/zeroDopplerTimeLastLine').text)
zero_dop_first_line = parse_timestring(
self._find('./imageGenerationParameters'
'/sarProcessingInformation'
'/zeroDopplerTimeFirstLine').text)
if look > 1: # SideOfTrack == 'L'
# we explicitly want negative time order
if zero_dop_first_line < zero_dop_last_line:
zero_dop_first_line, zero_dop_last_line = zero_dop_last_line, zero_dop_first_line
else:
# we explicitly want positive time order
if zero_dop_first_line > zero_dop_last_line:
zero_dop_first_line, zero_dop_last_line = zero_dop_last_line, zero_dop_first_line
col_spacing_zd = get_seconds(zero_dop_last_line,
zero_dop_first_line,
precision='us') / (image_data.NumCols - 1)
# zero doppler time of SCP relative to collect start
time_scp_zd = get_seconds(zero_dop_first_line, start_time, precision='us') + \
image_data.SCPPixel.Col*col_spacing_zd
if self.generation == 'RS2':
near_range = float(
self._find('./imageGenerationParameters'
'/sarProcessingInformation'
'/slantRangeNearEdge').text)
elif self.generation == 'RCM':
near_range = float(
self._find('./sceneAttributes'
'/imageAttributes'
'/slantRangeNearEdge').text)
else:
raise ValueError('unhandled generation {}'.format(self.generation))
inca = INCAType(R_CA_SCP=near_range +
(image_data.SCPPixel.Row * grid.Row.SS),
FreqZero=center_freq)
# doppler rate calculations
velocity = position.ARPPoly.derivative_eval(time_scp_zd, 1)
vel_ca_squared = numpy.sum(velocity * velocity)
# polynomial representing range as a function of range distance from SCP
r_ca = numpy.array([inca.R_CA_SCP, 1], dtype=numpy.float64)
# doppler rate coefficients
if self.generation == 'RS2':
doppler_rate_coeffs = numpy.array([
float(entry) for entry in self._find(
'./imageGenerationParameters'
'/dopplerRateValues'
'/dopplerRateValuesCoefficients').text.split()
],
dtype=numpy.float64)
doppler_rate_ref_time = float(
self._find('./imageGenerationParameters'
'/dopplerRateValues'
'/dopplerRateReferenceTime').text)
elif self.generation == 'RCM':
doppler_rate_coeffs = numpy.array([
float(entry) for entry in self._find(
'./dopplerRate'
'/dopplerRateEstimate'
'/dopplerRateCoefficients').text.split()
],
dtype=numpy.float64)
doppler_rate_ref_time = float(
self._find('./dopplerRate'
'/dopplerRateEstimate'
'/dopplerRateReferenceTime').text)
else:
raise ValueError('unhandled generation {}'.format(self.generation))
# the doppler_rate_coeffs represents a polynomial in time, relative to
# doppler_rate_ref_time.
# to construct the doppler centroid polynomial, we need to change scales
# to a polynomial in space, relative to SCP.
doppler_rate_poly = Poly1DType(Coefs=doppler_rate_coeffs)
alpha = 2.0 / speed_of_light
t_0 = doppler_rate_ref_time - alpha * inca.R_CA_SCP
dop_rate_scaled_coeffs = doppler_rate_poly.shift(t_0,
alpha,
return_poly=False)
# DRateSFPoly is then a scaled multiple of this scaled poly and r_ca above
coeffs = -numpy.convolve(dop_rate_scaled_coeffs, r_ca) / (
alpha * center_freq * vel_ca_squared)
inca.DRateSFPoly = Poly2DType(
Coefs=numpy.reshape(coeffs, (coeffs.size, 1)))
# modify a few of the other fields
ss_scale = numpy.sqrt(vel_ca_squared) * inca.DRateSFPoly[0, 0]
grid.Col.SS = col_spacing_zd * ss_scale
grid.Col.ImpRespBW = -look * doppler_bandwidth / ss_scale
inca.TimeCAPoly = Poly1DType(Coefs=[time_scp_zd, 1. / ss_scale])
# doppler centroid
if self.generation == 'RS2':
doppler_cent_coeffs = numpy.array([
float(entry) for entry in self._find(
'./imageGenerationParameters'
'/dopplerCentroid'
'/dopplerCentroidCoefficients').text.split()
],
dtype=numpy.float64)
doppler_cent_ref_time = float(
self._find('./imageGenerationParameters'
'/dopplerCentroid'
'/dopplerCentroidReferenceTime').text)
doppler_cent_time_est = parse_timestring(
self._find('./imageGenerationParameters'
'/dopplerCentroid'
'/timeOfDopplerCentroidEstimate').text)
elif self.generation == 'RCM':
doppler_cent_coeffs = numpy.array([
float(entry) for entry in self._find(
'./dopplerCentroid'
'/dopplerCentroidEstimate'
'/dopplerCentroidCoefficients').text.split()
],
dtype=numpy.float64)
doppler_cent_ref_time = float(
self._find('./dopplerCentroid'
'/dopplerCentroidEstimate'
'/dopplerCentroidReferenceTime').text)
doppler_cent_time_est = parse_timestring(
self._find('./dopplerCentroid'
'/dopplerCentroidEstimate'
'/timeOfDopplerCentroidEstimate').text)
else:
raise ValueError('unhandled generation {}'.format(self.generation))
doppler_cent_poly = Poly1DType(Coefs=doppler_cent_coeffs)
alpha = 2.0 / speed_of_light
t_0 = doppler_cent_ref_time - alpha * inca.R_CA_SCP
scaled_coeffs = doppler_cent_poly.shift(t_0, alpha, return_poly=False)
inca.DopCentroidPoly = Poly2DType(
Coefs=numpy.reshape(scaled_coeffs, (scaled_coeffs.size, 1)))
# adjust doppler centroid for spotlight, we need to add a second
# dimension to DopCentroidPoly
if collection_info.RadarMode.ModeType == 'SPOTLIGHT':
doppler_cent_est = get_seconds(doppler_cent_time_est,
start_time,
precision='us')
doppler_cent_col = (doppler_cent_est -
time_scp_zd) / col_spacing_zd
dop_poly = numpy.zeros((scaled_coeffs.shape[0], 2),
dtype=numpy.float64)
dop_poly[:, 0] = scaled_coeffs
dop_poly[0, 1] = -look * center_freq * alpha * numpy.sqrt(
vel_ca_squared) / inca.R_CA_SCP
# dopplerCentroid in native metadata was defined at specific column,
# which might not be our SCP column. Adjust so that SCP column is correct.
dop_poly[0, 0] = dop_poly[0, 0] - (dop_poly[0, 1] *
doppler_cent_col * grid.Col.SS)
inca.DopCentroidPoly = Poly2DType(Coefs=dop_poly)
grid.Col.DeltaKCOAPoly = Poly2DType(
Coefs=inca.DopCentroidPoly.get_array() * col_spacing_zd /
grid.Col.SS)
# compute grid.Col.DeltaK1/K2 from DeltaKCOAPoly
coeffs = grid.Col.DeltaKCOAPoly.get_array()[:, 0]
# get roots
roots = polynomial.polyroots(coeffs)
# construct range bounds (in meters)
range_bounds = (
numpy.array([0, image_data.NumRows - 1], dtype=numpy.float64) -
image_data.SCPPixel.Row) * grid.Row.SS
possible_ranges = numpy.copy(range_bounds)
useful_roots = ((roots > numpy.min(range_bounds)) &
(roots < numpy.max(range_bounds)))
if numpy.any(useful_roots):
possible_ranges = numpy.concatenate(
(possible_ranges, roots[useful_roots]), axis=0)
azimuth_bounds = (
numpy.array([0, (image_data.NumCols - 1)], dtype=numpy.float64) -
image_data.SCPPixel.Col) * grid.Col.SS
coords_az_2d, coords_rg_2d = numpy.meshgrid(azimuth_bounds,
possible_ranges)
possible_bounds_deltak = grid.Col.DeltaKCOAPoly(
coords_rg_2d, coords_az_2d)
grid.Col.DeltaK1 = numpy.min(
possible_bounds_deltak) - 0.5 * grid.Col.ImpRespBW
grid.Col.DeltaK2 = numpy.max(
possible_bounds_deltak) + 0.5 * grid.Col.ImpRespBW
# Wrapped spectrum
if (grid.Col.DeltaK1 < -0.5 / grid.Col.SS) or (grid.Col.DeltaK2 >
0.5 / grid.Col.SS):
grid.Col.DeltaK1 = -0.5 / abs(grid.Col.SS)
grid.Col.DeltaK2 = -grid.Col.DeltaK1
time_coa_poly = fit_time_coa_polynomial(inca,
image_data,
grid,
dop_rate_scaled_coeffs,
poly_order=2)
if collection_info.RadarMode.ModeType == 'SPOTLIGHT':
# using above was convenience, but not really sensible in spotlight mode
grid.TimeCOAPoly = Poly2DType(Coefs=[
[
time_coa_poly.Coefs[0, 0],
],
])
inca.DopCentroidPoly = None
elif collection_info.RadarMode.ModeType == 'STRIPMAP':
# fit TimeCOAPoly for grid
grid.TimeCOAPoly = time_coa_poly
inca.DopCentroidCOA = True
else:
raise ValueError('unhandled ModeType {}'.format(
collection_info.RadarMode.ModeType))
return RMAType(RMAlgoType='OMEGA_K', INCA=inca)
