def get_sicd_collection(self):
"""
Gets the list of sicd objects, one per polarimetric entry.
Returns
-------
Tuple[SICDType]
"""
nitf, collection_info = self._get_collection_info()
image_creation = self._get_image_creation()
image_data, geo_data = self._get_image_and_geo_data()
position = self._get_position()
grid = self._get_grid()
radar_collection = self._get_radar_collection()
timeline = self._get_timeline()
image_formation = self._get_image_formation(timeline, radar_collection)
scpcoa = self._get_scpcoa()
rma = self._get_rma_adjust_grid(scpcoa, grid, image_data, position,
collection_info)
radiometric = self._get_radiometric(image_data, grid)
base_sicd = SICDType(CollectionInfo=collection_info,
ImageCreation=image_creation,
GeoData=geo_data,
ImageData=image_data,
Position=position,
Grid=grid,
RadarCollection=radar_collection,
Timeline=timeline,
ImageFormation=image_formation,
SCPCOA=scpcoa,
RMA=rma,
Radiometric=radiometric)
if len(nitf) > 0:
base_sicd._NITF = nitf
self._update_geo_data(base_sicd)
base_sicd.derive() # derive all the fields
# now, make one copy per polarimetric entry, as appropriate
tx_pols, tx_rcv_pols = self._get_polarizations()
sicd_list = []
for i, entry in enumerate(tx_rcv_pols):
this_sicd = base_sicd.copy()
this_sicd.ImageFormation.RcvChanProc.ChanIndices = [
i + 1,
]
this_sicd.ImageFormation.TxRcvPolarizationProc = \
this_sicd.RadarCollection.RcvChannels[i].TxRcvPolarization
this_sicd.populate_rniirs(override=False)
sicd_list.append(this_sicd)
return tuple(sicd_list)
def get_sicd(self):
"""
Get the SICD metadata for the image.
Returns
-------
SICDType
"""
def convert_string_dict(dict_in):
# type: (dict) -> dict
dict_out = OrderedDict()
for key, val in dict_in.items():
if isinstance(val, string_types):
dict_out[key] = val
elif isinstance(val, int):
dict_out[key] = str(val)
elif isinstance(val, float):
dict_out[key] = '{0:0.16G}'.format(val)
else:
raise TypeError('Got unhandled type {}'.format(type(val)))
return dict_out
def extract_state_vector():
# type: () -> (numpy.ndarray, numpy.ndarray, numpy.ndarray)
vecs = collect['state']['state_vectors']
times = numpy.zeros((len(vecs), ), dtype=numpy.float64)
positions = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
velocities = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
for i, entry in enumerate(vecs):
times[i] = get_seconds(parse_timestring(entry['time'],
precision='ns'),
start_time,
precision='ns')
positions[i, :] = entry['position']
velocities[i, :] = entry['velocity']
return times, positions, velocities
def get_collection_info():
# type: () -> CollectionInfoType
coll_name = collect['platform']
start_dt = start_time.astype('datetime64[us]').astype(datetime)
mode = collect['mode'].strip().lower()
if mode == 'stripmap':
radar_mode = RadarModeType(ModeType='STRIPMAP')
elif mode == 'sliding_spotlight':
radar_mode = RadarModeType(ModeType='DYNAMIC STRIPMAP')
else:
raise ValueError('Got unhandled radar mode {}'.format(mode))
return CollectionInfoType(CollectorName=coll_name,
CoreName='{}{}{}'.format(
start_dt.strftime('%d%b%y').upper(),
coll_name,
start_dt.strftime('%H%M%S')),
RadarMode=radar_mode,
Classification='UNCLASSIFIED',
CollectType='MONOSTATIC')
def get_image_creation():
# type: () -> ImageCreationType
from sarpy.__about__ import __version__
return ImageCreationType(
Application=self._tiff_details.tags['Software'],
DateTime=parse_timestring(
self._img_desc_tags['processing_time'], precision='us'),
Profile='sarpy {}'.format(__version__),
Site='Unknown')
def get_image_data():
# type: () -> ImageDataType
img = collect['image']
rows = int(
img['columns']) # capella uses flipped row/column definition?
cols = int(img['rows'])
if img['data_type'] == 'CInt16':
pixel_type = 'RE16I_IM16I'
else:
raise ValueError('Got unhandled data_type {}'.format(
img['data_type']))
scp_pixel = (int(0.5 * rows), int(0.5 * cols))
if collect['radar']['pointing'] == 'left':
scp_pixel = (rows - scp_pixel[0] - 1, cols - scp_pixel[1] - 1)
return ImageDataType(NumRows=rows,
NumCols=cols,
FirstRow=0,
FirstCol=0,
PixelType=pixel_type,
FullImage=(rows, cols),
SCPPixel=scp_pixel)
def get_geo_data():
# type: () -> GeoDataType
return GeoDataType(SCP=SCPType(
ECF=collect['image']['center_pixel']['target_position']))
def get_position():
# type: () -> PositionType
px, py, pz = fit_position_xvalidation(state_time,
state_position,
state_velocity,
max_degree=6)
return PositionType(ARPPoly=XYZPolyType(X=px, Y=py, Z=pz))
def get_grid():
# type: () -> GridType
img = collect['image']
image_plane = 'OTHER'
grid_type = 'PLANE'
if self._img_desc_tags['product_type'] == 'SLC' and img[
'algorithm'] != 'backprojection':
image_plane = 'SLANT'
grid_type = 'RGZERO'
coa_time = parse_timestring(img['center_pixel']['center_time'],
precision='ns')
row_imp_rsp_bw = 2 * bw / speed_of_light
row = DirParamType(SS=img['pixel_spacing_column'],
ImpRespBW=row_imp_rsp_bw,
ImpRespWid=img['range_resolution'],
KCtr=2 * fc / speed_of_light,
DeltaK1=-0.5 * row_imp_rsp_bw,
DeltaK2=0.5 * row_imp_rsp_bw,
DeltaKCOAPoly=[
[
0.0,
],
],
WgtType=WgtTypeType(
WindowName=img['range_window']['name'],
Parameters=convert_string_dict(
img['range_window']['parameters'])))
# get timecoa value
timecoa_value = get_seconds(coa_time,
start_time) # TODO: constant?
# find an approximation for zero doppler spacing - necessarily rough for backprojected images
# find velocity at coatime
arp_velocity = position.ARPPoly.derivative_eval(timecoa_value,
der_order=1)
arp_speed = numpy.linalg.norm(arp_velocity)
col_ss = img['pixel_spacing_row']
dop_bw = img['processed_azimuth_bandwidth']
# ss_zd_s = col_ss/arp_speed
col = DirParamType(SS=col_ss,
ImpRespWid=img['azimuth_resolution'],
ImpRespBW=dop_bw / arp_speed,
KCtr=0,
WgtType=WgtTypeType(
WindowName=img['azimuth_window']['name'],
Parameters=convert_string_dict(
img['azimuth_window']['parameters'])))
# TODO: from Wade - account for numeric WgtFunct
return GridType(ImagePlane=image_plane,
Type=grid_type,
TimeCOAPoly=[
[
timecoa_value,
],
],
Row=row,
Col=col)
def get_radar_colection():
# type: () -> RadarCollectionType
radar = collect['radar']
freq_min = fc - 0.5 * bw
return RadarCollectionType(
TxPolarization=radar['transmit_polarization'],
TxFrequency=TxFrequencyType(Min=freq_min, Max=freq_min + bw),
Waveform=[
WaveformParametersType(
TxRFBandwidth=bw,
TxPulseLength=radar['pulse_duration'],
RcvDemodType='CHIRP',
ADCSampleRate=radar['sampling_frequency'],
TxFreqStart=freq_min)
],
RcvChannels=[
ChanParametersType(TxRcvPolarization='{}:{}'.format(
radar['transmit_polarization'],
radar['receive_polarization']))
])
def get_timeline():
# type: () -> TimelineType
prf = collect['radar']['prf'][0]['prf']
return TimelineType(CollectStart=start_time,
CollectDuration=duration,
IPP=[
IPPSetType(TStart=0,
TEnd=duration,
IPPStart=0,
IPPEnd=duration * prf,
IPPPoly=(0, prf)),
])
def get_image_formation():
# type: () -> ImageFormationType
radar = collect['radar']
algo = collect['image']['algorithm'].upper()
processings = None
if algo == 'BACKPROJECTION':
processings = [
ProcessingType(Type='Backprojected to DEM', Applied=True),
]
if algo not in ('PFA', 'RMA', 'RGAZCOMP'):
logging.warning(
'Image formation algorithm {} not one of the recognized SICD options, '
'being set to "OTHER".'.format(algo))
algo = 'OTHER'
return ImageFormationType(
RcvChanProc=RcvChanProcType(NumChanProc=1, PRFScaleFactor=1),
ImageFormAlgo=algo,
TStartProc=0,
TEndProc=duration,
TxRcvPolarizationProc='{}:{}'.format(
radar['transmit_polarization'],
radar['receive_polarization']),
TxFrequencyProc=TxFrequencyProcType(
MinProc=radar_collection.TxFrequency.Min,
MaxProc=radar_collection.TxFrequency.Max),
STBeamComp='NO',
ImageBeamComp='NO',
AzAutofocus='NO',
RgAutofocus='NO',
Processings=processings)
# TODO: From Wade - Radiometric is not suitable?
# extract general use information
collect = self._img_desc_tags['collect']
start_time = parse_timestring(collect['start_timestamp'],
precision='ns')
end_time = parse_timestring(collect['stop_timestamp'], precision='ns')
duration = get_seconds(end_time, start_time, precision='ns')
state_time, state_position, state_velocity = extract_state_vector()
bw = collect['radar']['pulse_bandwidth']
fc = collect['radar']['center_frequency']
# define the sicd elements
collection_info = get_collection_info()
image_creation = get_image_creation()
image_data = get_image_data()
geo_data = get_geo_data()
position = get_position()
grid = get_grid()
radar_collection = get_radar_colection()
timeline = get_timeline()
image_formation = get_image_formation()
sicd = SICDType(CollectionInfo=collection_info,
ImageCreation=image_creation,
ImageData=image_data,
GeoData=geo_data,
Position=position,
Grid=grid,
RadarCollection=radar_collection,
Timeline=timeline,
ImageFormation=image_formation)
sicd.derive()
# this would be a rough estimate - waiting for radiometric data
# sicd.populate_rniirs(override=False)
return sicd
def get_sicd(self):
"""
Gets the SICD structure.
Returns
-------
Tuple[SICDType, tuple, tuple]
The sicd structure, the data size argument, and the symmetry argument.
"""
def get_collection_info():
# type: () -> CollectionInfoType
return CollectionInfoType(
CollectorName=_stringify(hf['satellite_name'][()]),
CoreName=_stringify(hf['product_name'][()]),
CollectType='MONOSTATIC',
Classification='UNCLASSIFIED',
RadarMode=RadarModeType(
ModeType=_stringify(hf['acquisition_mode'][()]).upper(),
ModeID=_stringify(hf['product_type'][()])))
def get_image_creation():
# type: () -> ImageCreationType
from sarpy.__about__ import __version__
return ImageCreationType(
Application='ICEYE_P_{}'.format(hf['processor_version'][()]),
DateTime=_parse_time(hf['processing_time'][()]),
Site='Unknown',
Profile='sarpy {}'.format(__version__))
def get_image_data():
# type: () -> ImageDataType
samp_prec = _stringify(hf['sample_precision'][()])
if samp_prec.upper() == 'INT16':
pixel_type = 'RE16I_IM16I'
elif samp_prec.upper() == 'FLOAT32':
pixel_type = 'RE32F_IM32F'
else:
raise ValueError(
'Got unhandled sample precision {}'.format(samp_prec))
num_rows = int_func(number_of_range_samples)
num_cols = int_func(number_of_azimuth_samples)
scp_row = int_func(coord_center[0]) - 1
scp_col = int_func(coord_center[1]) - 1
if 0 < scp_col < num_rows - 1:
if look_side == 'left':
scp_col = num_cols - scp_col - 1
else:
# early ICEYE processing bug led to nonsensical SCP
scp_col = int_func(num_cols / 2.0)
return ImageDataType(PixelType=pixel_type,
NumRows=num_rows,
NumCols=num_cols,
FirstRow=0,
FirstCol=0,
FullImage=(num_rows, num_cols),
SCPPixel=(scp_row, scp_col))
def get_geo_data():
# type: () -> GeoDataType
# NB: the remainder will be derived.
return GeoDataType(SCP=SCPType(
LLH=[coord_center[2], coord_center[3], avg_scene_height]))
def get_timeline():
# type: () -> TimelineType
acq_prf = hf['acquisition_prf'][()]
return TimelineType(CollectStart=start_time,
CollectDuration=duration,
IPP=[
IPPSetType(index=0,
TStart=0,
TEnd=duration,
IPPStart=0,
IPPEnd=int_func(
round(acq_prf * duration)),
IPPPoly=[0, acq_prf]),
])
def get_position():
# type: () -> PositionType
# fetch the state information
times_str = hf['state_vector_time_utc'][:, 0]
times = numpy.zeros((times_str.shape[0], ), dtype='float64')
positions = numpy.zeros((times.size, 3), dtype='float64')
velocities = numpy.zeros((times.size, 3), dtype='float64')
for i, entry in enumerate(times_str):
times[i] = get_seconds(_parse_time(entry),
start_time,
precision='us')
positions[:, 0], positions[:, 1], positions[:, 2] = hf[
'posX'][:], hf['posY'][:], hf['posZ'][:]
velocities[:, 0], velocities[:, 1], velocities[:, 2] = hf[
'velX'][:], hf['velY'][:], hf['velZ'][:]
# fir the the position polynomial using cross validation
P_x, P_y, P_z = fit_position_xvalidation(times,
positions,
velocities,
max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=P_x, Y=P_y, Z=P_z))
def get_radar_collection():
# type : () -> RadarCollection
return RadarCollectionType(
TxPolarization=tx_pol,
TxFrequency=TxFrequencyType(Min=min_freq, Max=max_freq),
Waveform=[
WaveformParametersType(
TxFreqStart=min_freq,
TxRFBandwidth=tx_bandwidth,
TxPulseLength=hf['chirp_duration'][()],
ADCSampleRate=hf['range_sampling_rate'][()],
RcvDemodType='CHIRP',
RcvFMRate=0,
index=1)
],
RcvChannels=[
ChanParametersType(TxRcvPolarization=polarization, index=1)
])
def get_image_formation():
# type: () -> ImageFormationType
return ImageFormationType(
TxRcvPolarizationProc=polarization,
ImageFormAlgo='RMA',
TStartProc=0,
TEndProc=duration,
TxFrequencyProc=TxFrequencyProcType(MinProc=min_freq,
MaxProc=max_freq),
STBeamComp='NO',
ImageBeamComp='SV',
AzAutofocus='NO',
RgAutofocus='NO',
RcvChanProc=RcvChanProcType(NumChanProc=1,
PRFScaleFactor=1,
ChanIndices=[
1,
]),
)
def get_radiometric():
# type: () -> RadiometricType
return RadiometricType(BetaZeroSFPoly=[
[
float(hf['calibration_factor'][()]),
],
])
def calculate_drate_sf_poly():
r_ca_coeffs = numpy.array([r_ca_scp, 1], dtype='float64')
dop_rate_coeffs = hf['doppler_rate_coeffs'][:]
# Prior to ICEYE 1.14 processor, absolute value of Doppler rate was
# provided, not true Doppler rate. Doppler rate should always be negative
if dop_rate_coeffs[0] > 0:
dop_rate_coeffs *= -1
dop_rate_poly = Poly1DType(Coefs=dop_rate_coeffs)
# now adjust to create
t_drate_ca_poly = dop_rate_poly.shift(t_0=zd_ref_time -
rg_time_scp,
alpha=2 / speed_of_light,
return_poly=False)
return t_drate_ca_poly, -polynomial.polymul(
t_drate_ca_poly, r_ca_coeffs) * speed_of_light / (
2 * center_freq * vm_ca_sq)
def calculate_doppler_polys():
# extract doppler centroid coefficients
dc_estimate_coeffs = hf['dc_estimate_coeffs'][:]
dc_time_str = hf['dc_estimate_time_utc'][:, 0]
dc_zd_times = numpy.zeros((dc_time_str.shape[0], ),
dtype='float64')
for i, entry in enumerate(dc_time_str):
dc_zd_times[i] = get_seconds(_parse_time(entry),
start_time,
precision='us')
# create a sampled doppler centroid
samples = 49 # copied from corresponding matlab, we just need enough for appropriate refitting
# create doppler time samples
diff_time_rg = first_pixel_time - zd_ref_time + \
numpy.linspace(0, number_of_range_samples/range_sampling_rate, samples)
# doppler centroid samples definition
dc_sample_array = numpy.zeros((samples, dc_zd_times.size),
dtype='float64')
for i, coeffs in enumerate(dc_estimate_coeffs):
dc_sample_array[:,
i] = polynomial.polyval(diff_time_rg, coeffs)
# create arrays for range/azimuth from scp in meters
azimuth_scp_m, range_scp_m = numpy.meshgrid(
col_ss * (dc_zd_times - zd_time_scp) / ss_zd_s,
(diff_time_rg + zd_ref_time - rg_time_scp) * speed_of_light /
2)
# fit the doppler centroid sample array
x_order = min(3, range_scp_m.shape[0] - 1)
y_order = min(3, range_scp_m.shape[1] - 1)
t_dop_centroid_poly, residuals, rank, sing_values = two_dim_poly_fit(
range_scp_m,
azimuth_scp_m,
dc_sample_array,
x_order=x_order,
y_order=y_order,
x_scale=1e-3,
y_scale=1e-3,
rcond=1e-40)
logging.info(
'The dop_centroid_poly fit details:\nroot mean square '
'residuals = {}\nrank = {}\nsingular values = {}'.format(
residuals, rank, sing_values))
# define and fit the time coa array
doppler_rate_sampled = polynomial.polyval(azimuth_scp_m,
drate_ca_poly)
time_coa = dc_zd_times + dc_sample_array / doppler_rate_sampled
t_time_coa_poly, residuals, rank, sing_values = two_dim_poly_fit(
range_scp_m,
azimuth_scp_m,
time_coa,
x_order=x_order,
y_order=y_order,
x_scale=1e-3,
y_scale=1e-3,
rcond=1e-40)
logging.info(
'The time_coa_poly fit details:\nroot mean square '
'residuals = {}\nrank = {}\nsingular values = {}'.format(
residuals, rank, sing_values))
return t_dop_centroid_poly, t_time_coa_poly
def get_rma():
# type: () -> 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_grid():
# type: () -> GridType
time_coa_poly = Poly2DType(Coefs=time_coa_poly_coeffs)
if collect_info.RadarMode.ModeType == 'SPOTLIGHT':
time_coa_poly = Poly2DType(Coefs=[
[
float(time_coa_poly_coeffs[0, 0]),
],
])
row_win = _stringify(hf['window_function_range'][()])
if row_win == 'NONE':
row_win = 'UNIFORM'
row = DirParamType(SS=row_ss,
Sgn=-1,
KCtr=2 * center_freq / speed_of_light,
ImpRespBW=2 * tx_bandwidth / speed_of_light,
DeltaKCOAPoly=Poly2DType(Coefs=[[
0,
]]),
WgtType=WgtTypeType(WindowName=row_win))
col_win = _stringify(hf['window_function_azimuth'][()])
if col_win == 'NONE':
col_win = 'UNIFORM'
col = DirParamType(
SS=col_ss,
Sgn=-1,
KCtr=0,
ImpRespBW=col_imp_res_bw,
WgtType=WgtTypeType(WindowName=col_win),
DeltaKCOAPoly=Poly2DType(Coefs=dop_centroid_poly_coeffs *
ss_zd_s / col_ss))
return GridType(Type='RGZERO',
ImagePlane='SLANT',
TimeCOAPoly=time_coa_poly,
Row=row,
Col=col)
def correct_scp():
scp_pixel = sicd.ImageData.SCPPixel.get_array()
scp_ecf = sicd.project_image_to_ground(scp_pixel,
projection_type='HAE')
sicd.update_scp(scp_ecf, coord_system='ECF')
with h5py.File(self._file_name, 'r') as hf:
# some common use variables
look_side = _stringify(hf['look_side'][()])
coord_center = hf['coord_center'][:]
avg_scene_height = float(hf['avg_scene_height'][()])
start_time = _parse_time(hf['acquisition_start_utc'][()])
end_time = _parse_time(hf['acquisition_end_utc'][()])
duration = get_seconds(end_time, start_time, precision='us')
center_freq = float(hf['carrier_frequency'][()])
tx_bandwidth = float(hf['chirp_bandwidth'][()])
min_freq = center_freq - 0.5 * tx_bandwidth
max_freq = center_freq + 0.5 * tx_bandwidth
pol_temp = _stringify(hf['polarization'][()])
tx_pol = pol_temp[0]
rcv_pol = pol_temp[1]
polarization = tx_pol + ':' + rcv_pol
first_pixel_time = float(hf['first_pixel_time'][()])
near_range = first_pixel_time * speed_of_light / 2
number_of_range_samples = float(hf['number_of_range_samples'][()])
number_of_azimuth_samples = float(
hf['number_of_azimuth_samples'][()])
range_sampling_rate = float(hf['range_sampling_rate'][()])
row_ss = speed_of_light / (2 * range_sampling_rate)
# define the sicd elements
collect_info = get_collection_info()
image_creation = get_image_creation()
image_data = get_image_data()
geo_data = get_geo_data()
timeline = get_timeline()
position = get_position()
radar_collection = get_radar_collection()
image_formation = get_image_formation()
radiometric = get_radiometric()
# calculate some zero doppler parameters
ss_zd_s = float(hf['azimuth_time_interval'][()])
if look_side == 'left':
ss_zd_s *= -1
zero_doppler_left = _parse_time(hf['zerodoppler_end_utc'][()])
else:
zero_doppler_left = _parse_time(
hf['zerodoppler_start_utc'][()])
dop_bw = hf['total_processed_bandwidth_azimuth'][()]
zd_time_scp = get_seconds(zero_doppler_left, start_time, precision='us') + \
image_data.SCPPixel.Col*ss_zd_s
zd_ref_time = first_pixel_time + number_of_range_samples / (
2 * range_sampling_rate)
vel_scp = position.ARPPoly.derivative_eval(zd_time_scp,
der_order=1)
vm_ca_sq = numpy.sum(vel_scp * vel_scp)
rg_time_scp = first_pixel_time + image_data.SCPPixel.Row / range_sampling_rate
r_ca_scp = rg_time_scp * speed_of_light / 2
# calculate the doppler rate sf polynomial
drate_ca_poly, drate_sf_poly_coefs = calculate_drate_sf_poly()
# calculate some doppler dependent grid parameters
col_ss = float(
numpy.sqrt(vm_ca_sq) * abs(ss_zd_s) * drate_sf_poly_coefs[0])
col_imp_res_bw = dop_bw * abs(ss_zd_s) / col_ss
time_ca_poly_coeffs = [zd_time_scp, ss_zd_s / col_ss]
# calculate the doppler polynomials
dop_centroid_poly_coeffs, time_coa_poly_coeffs = calculate_doppler_polys(
)
# finish definition of sicd elements
rma = get_rma()
grid = get_grid()
sicd = SICDType(CollectionInfo=collect_info,
ImageCreation=image_creation,
ImageData=image_data,
GeoData=geo_data,
Timeline=timeline,
Position=position,
RadarCollection=radar_collection,
ImageFormation=image_formation,
Radiometric=radiometric,
RMA=rma,
Grid=grid)
# adjust the scp location
correct_scp()
# derive sicd fields
sicd.derive()
# TODO: RNIIRS?
data_size = (image_data.NumCols, image_data.NumRows)
symmetry = (True, False,
True) if look_side == 'left' else (False, False, True)
return sicd, data_size, symmetry
def get_sicd(self):
"""
Get the SICD metadata for the image.
Returns
-------
SICDType
"""
def convert_string_dict(dict_in):
# type: (dict) -> dict
dict_out = OrderedDict()
for key, val in dict_in.items():
if isinstance(val, str):
dict_out[key] = val
elif isinstance(val, int):
dict_out[key] = str(val)
elif isinstance(val, float):
dict_out[key] = '{0:0.17G}'.format(val)
else:
raise TypeError('Got unhandled type {}'.format(type(val)))
return dict_out
def extract_state_vector():
# type: () -> (numpy.ndarray, numpy.ndarray, numpy.ndarray)
vecs = collect['state']['state_vectors']
times = numpy.zeros((len(vecs), ), dtype=numpy.float64)
positions = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
velocities = numpy.zeros((len(vecs), 3), dtype=numpy.float64)
for i, entry in enumerate(vecs):
times[i] = get_seconds(parse_timestring(entry['time'],
precision='ns'),
start_time,
precision='ns')
positions[i, :] = entry['position']
velocities[i, :] = entry['velocity']
return times, positions, velocities
def get_radar_parameter(name):
if name in radar:
return radar[name]
if len(radar_time_varying) > 0:
element = radar_time_varying[0]
if name in element:
return element[name]
raise ValueError(
'Unable to determine radar parameter `{}`'.format(name))
def get_collection_info():
# type: () -> CollectionInfoType
coll_name = collect['platform']
mode = collect['mode'].strip().lower()
if mode == 'stripmap':
radar_mode = RadarModeType(ModeType='STRIPMAP', ModeID=mode)
elif mode == 'spotlight':
radar_mode = RadarModeType(ModeType='SPOTLIGHT', ModeID=mode)
elif mode == 'sliding_spotlight':
radar_mode = RadarModeType(ModeType='DYNAMIC STRIPMAP',
ModeID=mode)
else:
raise ValueError('Got unhandled radar mode {}'.format(mode))
return CollectionInfoType(CollectorName=coll_name,
CoreName=collect['collect_id'],
RadarMode=radar_mode,
Classification='UNCLASSIFIED',
CollectType='MONOSTATIC')
def get_image_creation():
# type: () -> ImageCreationType
from sarpy.__about__ import __version__
return ImageCreationType(
Application=self._tiff_details.tags['Software'],
DateTime=parse_timestring(
self._img_desc_tags['processing_time'], precision='us'),
Profile='sarpy {}'.format(__version__),
Site='Unknown')
def get_image_data():
# type: () -> ImageDataType
rows = int(
img['columns']) # capella uses flipped row/column definition?
cols = int(img['rows'])
if img['data_type'] == 'CInt16':
pixel_type = 'RE16I_IM16I'
else:
raise ValueError('Got unhandled data_type {}'.format(
img['data_type']))
scp_pixel = (int(0.5 * rows), int(0.5 * cols))
if radar['pointing'] == 'left':
scp_pixel = (rows - scp_pixel[0] - 1, cols - scp_pixel[1] - 1)
return ImageDataType(NumRows=rows,
NumCols=cols,
FirstRow=0,
FirstCol=0,
PixelType=pixel_type,
FullImage=(rows, cols),
SCPPixel=scp_pixel)
def get_geo_data():
# type: () -> GeoDataType
return GeoDataType(SCP=SCPType(
ECF=img['center_pixel']['target_position']))
def get_position():
# type: () -> PositionType
px, py, pz = fit_position_xvalidation(state_time,
state_position,
state_velocity,
max_degree=8)
return PositionType(ARPPoly=XYZPolyType(X=px, Y=py, Z=pz))
def get_grid():
# type: () -> GridType
def get_weight(window_dict):
window_name = window_dict['name']
if window_name.lower() == 'rectangular':
return WgtTypeType(WindowName='UNIFORM'), None
elif window_name.lower() == 'avci-nacaroglu':
return WgtTypeType(
WindowName=window_name.upper(),
Parameters=convert_string_dict(window_dict['parameters'])), \
avci_nacaroglu_window(64, alpha=window_dict['parameters']['alpha'])
else:
return WgtTypeType(WindowName=window_name,
Parameters=convert_string_dict(
window_dict['parameters'])), None
image_plane = 'SLANT'
grid_type = 'RGZERO'
coa_time = parse_timestring(img['center_pixel']['center_time'],
precision='ns')
row_bw = img.get('processed_range_bandwidth', bw)
row_imp_rsp_bw = 2 * row_bw / speed_of_light
row_wgt, row_wgt_funct = get_weight(img['range_window'])
row = DirParamType(SS=img['image_geometry']['delta_range_sample'],
Sgn=-1,
ImpRespBW=row_imp_rsp_bw,
ImpRespWid=img['range_resolution'],
KCtr=2 * fc / speed_of_light,
DeltaK1=-0.5 * row_imp_rsp_bw,
DeltaK2=0.5 * row_imp_rsp_bw,
DeltaKCOAPoly=[
[
0.0,
],
],
WgtFunct=row_wgt_funct,
WgtType=row_wgt)
# get timecoa value
timecoa_value = get_seconds(coa_time, start_time)
# find an approximation for zero doppler spacing - necessarily rough for backprojected images
col_ss = img['pixel_spacing_row']
dop_bw = img['processed_azimuth_bandwidth']
col_wgt, col_wgt_funct = get_weight(img['azimuth_window'])
col = DirParamType(SS=col_ss,
Sgn=-1,
ImpRespWid=img['azimuth_resolution'],
ImpRespBW=dop_bw * abs(ss_zd_s) / col_ss,
KCtr=0,
WgtFunct=col_wgt_funct,
WgtType=col_wgt)
# TODO:
# column deltakcoa poly - it's in there at ["image"]["frequency_doppler_centroid_polynomial"]
return GridType(ImagePlane=image_plane,
Type=grid_type,
TimeCOAPoly=[
[
timecoa_value,
],
],
Row=row,
Col=col)
def get_radar_collection():
# type: () -> RadarCollectionType
freq_min = fc - 0.5 * bw
return RadarCollectionType(
TxPolarization=radar['transmit_polarization'],
TxFrequency=(freq_min, freq_min + bw),
Waveform=[
WaveformParametersType(
TxRFBandwidth=bw,
TxPulseLength=get_radar_parameter('pulse_duration'),
RcvDemodType='CHIRP',
ADCSampleRate=radar['sampling_frequency'],
TxFreqStart=freq_min)
],
RcvChannels=[
ChanParametersType(TxRcvPolarization='{}:{}'.format(
radar['transmit_polarization'],
radar['receive_polarization']))
])
def get_timeline():
# type: () -> TimelineType
prf = radar['prf'][0]['prf']
return TimelineType(CollectStart=start_time,
CollectDuration=duration,
IPP=[
IPPSetType(TStart=0,
TEnd=duration,
IPPStart=0,
IPPEnd=duration * prf,
IPPPoly=(0, prf)),
])
def get_image_formation():
# type: () -> ImageFormationType
algo = img['algorithm'].upper()
processings = None
if algo == 'BACKPROJECTION':
processings = [
ProcessingType(Type='Backprojected to DEM', Applied=True),
]
else:
logger.warning('Got unexpected algorithm, the results for the '
'sicd struture might be unexpected')
if algo not in ('PFA', 'RMA', 'RGAZCOMP'):
logger.warning(
'Image formation algorithm {} not one of the recognized SICD options, '
'being set to "OTHER".'.format(algo))
algo = 'OTHER'
return ImageFormationType(
RcvChanProc=RcvChanProcType(NumChanProc=1, PRFScaleFactor=1),
ImageFormAlgo=algo,
TStartProc=0,
TEndProc=duration,
TxRcvPolarizationProc='{}:{}'.format(
radar['transmit_polarization'],
radar['receive_polarization']),
TxFrequencyProc=(radar_collection.TxFrequency.Min,
radar_collection.TxFrequency.Max),
STBeamComp='NO',
ImageBeamComp='NO',
AzAutofocus='NO',
RgAutofocus='NO',
Processings=processings)
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_radiometric():
# type: () -> Union[None, RadiometricType]
if img['radiometry'].lower() != 'beta_nought':
logger.warning('Got unrecognized Capella radiometry {},\n\t'
'skipping the radiometric metadata'.format(
img['radiometry']))
return None
return RadiometricType(BetaZeroSFPoly=[
[
img['scale_factor']**2,
],
])
def add_noise():
if sicd.Radiometric is None:
return
nesz_raw = numpy.array(img['nesz_polynomial']['coefficients'],
dtype='float64')
test_value = polynomial.polyval(rma.INCA.R_CA_SCP, nesz_raw)
if abs(test_value - img['nesz_peak']) > 100:
# this polynomial reversed in early versions, so reverse if evaluated results are nonsense
nesz_raw = nesz_raw[::-1]
nesz_poly_raw = Poly2DType(Coefs=numpy.reshape(nesz_raw, (-1, 1)))
noise_coeffs = nesz_poly_raw.shift(-rma.INCA.R_CA_SCP,
1,
0,
1,
return_poly=False)
# this is in nesz units, so shift to absolute units
noise_coeffs[0] -= 10 * numpy.log10(
sicd.Radiometric.SigmaZeroSFPoly[0, 0])
sicd.Radiometric.NoiseLevel = NoiseLevelType_(
NoiseLevelType='ABSOLUTE', NoisePoly=noise_coeffs)
# extract general use information
collect = self._img_desc_tags['collect']
img = collect['image']
radar = collect['radar']
radar_time_varying = radar.get('time_varying_parameters', [])
start_time = parse_timestring(collect['start_timestamp'],
precision='ns')
end_time = parse_timestring(collect['stop_timestamp'], precision='ns')
duration = get_seconds(end_time, start_time, precision='ns')
state_time, state_position, state_velocity = extract_state_vector()
bw = get_radar_parameter('pulse_bandwidth')
fc = get_radar_parameter('center_frequency')
ss_zd_s = img['image_geometry']['delta_line_time']
look = -1 if radar['pointing'] == 'right' else 1
# define the sicd elements
collection_info = get_collection_info()
image_creation = get_image_creation()
image_data = get_image_data()
geo_data = get_geo_data()
position = get_position()
grid = get_grid()
radar_collection = get_radar_collection()
timeline = get_timeline()
image_formation = get_image_formation()
rma = get_rma()
radiometric = get_radiometric()
sicd = SICDType(CollectionInfo=collection_info,
ImageCreation=image_creation,
ImageData=image_data,
GeoData=geo_data,
Position=position,
Grid=grid,
RadarCollection=radar_collection,
Timeline=timeline,
ImageFormation=image_formation,
RMA=rma,
Radiometric=radiometric)
sicd.derive()
add_noise()
sicd.populate_rniirs(override=False)
return sicd