V0.5 LT1AB CPP 适配完成，调整了部分 python代码中的部分错误
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@ -1,7 +1,7 @@
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<?xml version='1.0' encoding='utf-8'?>
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<Root>
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<TaskID>CSAR_202107275419_0001-0</TaskID>
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<WorkSpace>D:\micro\LWork\</WorkSpace>
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<WorkSpace>D:\micro\WorkSpace\</WorkSpace>
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<AlgCompt>
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<DataTransModel>File</DataTransModel>
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<Artificial>ElementAlg</Artificial>
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@ -45,7 +45,7 @@
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<ParaType>File</ParaType>
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<DataType>tar.gz</DataType>
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<ParaSource>Cal</ParaSource>
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<ParaValue>E:\MicroWorkspace\LT1B\LT230919\LT1B_MONO_MYC_STRIP4_005860_E130.9_N47.7_20230327_SLC_AHV_L1A_0000086966.tar.gz</ParaValue>
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<ParaValue>D:\micro\LTOrth\LT1B_MONO_KSC_STRIP2_007065_E100.8_N26.6_20230616_SLC_HH_L1A_0000135014.tar.gz</ParaValue>
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<EnModification>True</EnModification>
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<EnMultipleChoice>False</EnMultipleChoice>
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<Control>File</Control>
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@ -60,7 +60,7 @@
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<ParaType>File</ParaType>
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<DataType>File</DataType>
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<ParaSource>Cal</ParaSource>
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<ParaValue>E:\MicroWorkspace\LT1B\LT230919\dem</ParaValue>
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<ParaValue>D:\micro\LTOrth\dem</ParaValue>
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<EnModification>True</EnModification>
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<EnMultipleChoice>True</EnMultipleChoice>
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<Control>File</Control>
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@ -92,7 +92,7 @@
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<ParaType>File</ParaType>
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<DataType>tar.gz</DataType>
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<ParaSource>Cal</ParaSource>
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<ParaValue>D:\micro\LWork\Ortho\Output\LT1B_MONO_MYC_STRIP4_005860_E130.9_N47.7_20230327_SLC_AHV_L1A_0000086966-ortho.tar.gz</ParaValue>
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<ParaValue>D:\micro\WorkSpace\Ortho\Output\LT1B_MONO_KSC_STRIP2_007065_E100.8_N26.6_20230616_SLC_HH_L1A_0000135014-ortho.tar.gz</ParaValue>
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<MaxValue>DEFAULT</MaxValue>
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<MinValue>DEFAULT</MinValue>
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<OptionValue>DEFAULT</OptionValue>
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@ -1131,11 +1131,11 @@ class Orthorectification(object):
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FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['Azspace']['NodePath']))
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# 4、影像的近斜距
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NearRange = float(FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['NearRange']['NodePath']))
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NearRange = NearRange * LightSpeed / 2
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NearRangeTime = float(FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['NearRange']['NodePath']))
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NearRange = NearRangeTime * LightSpeed / 2
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HeaderInformation_json['ImageInformation']['NearRange'] = float(NearRange)
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FarRange = float(FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['FarRange']['NodePath']))
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FarRange = FarRange * LightSpeed / 2
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FarRangeTime = float(FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['FarRange']['NodePath']))
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FarRange = FarRangeTime * LightSpeed / 2
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# 远斜距
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HeaderInformation_json['ImageInformation']['FarRange'] = float(FarRange)
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# 5、入射角
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@ -1183,7 +1183,7 @@ class Orthorectification(object):
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DopplerRates = FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['DopplerRate']['NodePath'])
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DopplerRate = []
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for doppler in DopplerRates:
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DopplerRate.append(doppler.get('#text'))
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DopplerRate.append(float(doppler.get('#text'))) # 嘉兴
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HeaderInformation_json['ImageInformation']['DopplerRate'] = DopplerRate
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# 11、参考斜距
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# HeaderInformation_json['ImageInformation']['refRange'] = float(
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@ -1201,16 +1201,14 @@ class Orthorectification(object):
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self.config['imageinfo']['StartImageTime']['Format']).timestamp()
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lastpixeltime=float(FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['FarRange']['NodePath']))
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firstpixeltime=float(FindInfomationFromJson(HeaderFile_dom_json, self.config['imageinfo']['NearRange']['NodePath']))
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eqvFs = ((ImageHeight - 2)/(lastpixeltime - firstpixeltime))/2
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eqvFs = ((HeaderInformation_json['ImageInformation']['width'] - 1)/(lastpixeltime - firstpixeltime))
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# 12、PRF
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# HeaderInformation_json['PRF'] = float(
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# FindInfomationFromJson(HeaderFile_dom_json, self.config['sensor']['PRF']['NodePath']))
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HeaderInformation_json['PRF'] = float((ImageHeight-2)/(EndImageTime-StartImageTime)) # eqvPRF
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HeaderInformation_json['PRF'] = float((ImageHeight-1)/(EndImageTime-StartImageTime)) # eqvPRF
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HeaderInformation_json['Fs'] = float(eqvFs) # FindInfomationFromJson(HeaderFile_dom_json, self.config['sensor']['Fs']['NodePath'])
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# 13、中心时间
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HeaderInformation_json['ImageInformation']['CenterTime'] = (EndImageTime + StartImageTime)/2
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@ -1502,7 +1500,7 @@ class IndirectOrthorectification(Orthorectification):
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fp.write("{}\n".format(self.header_info['ImageInformation']['StartTime']))
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fp.write("{}\n".format(self.header_info['PRF']))
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fp.write("{}\n".format(self.header_info['ImageInformation']['NearRange'])) # LT
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fp.write("{}\n".format(self.Fs/2))
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fp.write("{}\n".format(self.Fs))
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fp.write("{}\n".format(self.header_info['ImageInformation']['DopplerParametersReferenceTime']))
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#fp.write("{}\n".format(self.widthspace))
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@ -1709,17 +1707,20 @@ class IndirectOrthorectification(Orthorectification):
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print("==========================================================================")
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def test_PSTN(self):
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landpoint=np.array([-1945072.5,5344083.0,2878316.0]).reshape(1,3)
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landpoint=np.array(OrthoAuxData.LLA2XYZ(100.3470449857,27.4318507628,3451))
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landpoint=landpoint.reshape(1,3)
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# 间接定位法所需的参数
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Doppler_d = np.array(self.header_info['ImageInformation']['DopplerCentroidCoefficients']).reshape(-1, 1)
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Doppler_d = np.array(self.header_info['ImageInformation']['DopplerRate']).reshape(-1, 1)
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LightSpeed = self.config['LightSpeed']
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T0 = self.header_info['ImageInformation']['refRange'] * 2 / LightSpeed
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T0 = self.header_info['ImageInformation']['DopplerParametersReferenceTime']
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lamda = self.header_info['ImageInformation']['lambda']
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StartTime = self.header_info['ImageInformation']['StartTime']
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PRF = self.header_info['PRF']
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Fs=self.Fs
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R0 = self.header_info['ImageInformation']['NearRange']
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r,c,ti=self.PSTN_block(landpoint, Doppler_d, StartTime, lamda, T0, LightSpeed, PRF, R0)
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r,c,ti=self.PSTN_block(landpoint, Doppler_d, StartTime, lamda, T0, LightSpeed, PRF, R0,Fs)
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print(r,c,ti)
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pass
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def findInitPoint(self,sim_mask,dem_gt,sim_r_tif,sim_c_tif,sampling_f):
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@ -2223,7 +2224,7 @@ class IndirectOrthorectification(Orthorectification):
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ti[start_i:end_i,:]=ti_temp.copy()
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return r,c,ti
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def PSTN_block(self, TargetPostion, Doppler_d, StartTime, lamda, T0, LightSpeed, PRF, R0):
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def PSTN_block(self, TargetPostion, Doppler_d, StartTime, lamda, T0, LightSpeed, PRF, R0,Fs):
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'''
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间接求解方法，使用矩阵方法
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args:
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@ -2276,15 +2277,15 @@ class IndirectOrthorectification(Orthorectification):
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# 斜距
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R = np.sqrt(np.sum(R_sc ** 2, axis=1)).reshape(-1, 1) # nx1
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# 根据多普勒数值求解公式求解多普勒频移值
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TSR= R * (2 / LightSpeed) - T0 # nx1
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TSR= R * (2 / LightSpeed) # - T0 # nx1
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TSRx[:,0]=TSR[:,0]**0 # nx5
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TSRx[:,1]=TSR[:,0]**1
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TSRx[:,2]=TSR[:,0]**2
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TSRx[:,3]=TSR[:,0]**3
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TSRx[:,4]=TSR[:,0]**4
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# TSRx[:,4]=TSR[:,0]**4
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# 根据多普勒物理公式求解求解多普勒频移值
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FdTheory=-2*np.reciprocal(lamda*R)*np.sum(R_sc * V_sc, axis=1).reshape(-1, 1) # nx1
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FdNumerical= np.matmul(TSRx, Doppler_d) # nx1
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FdNumerical= 0;np.matmul(TSRx, Doppler_d) # nx1
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FdTheory_grad = np.reciprocal((FdTheory_grad - FdTheory) * dt_rec) # nx1
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inc_t = (FdTheory - FdNumerical) * FdTheory_grad #(FdTheory - FdNumerical) * (1 / FdTheory_grad) # nx1
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@ -2295,16 +2296,14 @@ class IndirectOrthorectification(Orthorectification):
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#del Rs,R_sc,V_sc,R,satelliteSpaceState,FdTheory_grad,FdNumerical,inc_t
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#print("当前python程序，{} step， inc_t {} 求解内存占用 {}G".format(i,np.max(inc_t),psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
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# 计算行号
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ti_=copy.deepcopy(ti.copy())
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r_ = (ti_ - StartTime)*PRF
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r= (ti - StartTime)*PRF
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delta_t_light=delta_t*LightSpeed
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r=copy.deepcopy(r_.copy())
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del r_
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c_ = (R - R0) * (1 / delta_R)
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c=copy.deepcopy(c_.copy())
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del c_
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ti = copy.deepcopy(ti_.copy())
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del ti_
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c = 2*(R - R0) / LightSpeed * Fs
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"""
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<refRow>1</refRow>
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<refColumn>26669</refColumn>
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"""
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return r, c, ti
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pass
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@ -2983,3 +2982,11 @@ class IndirectOrthorectification(Orthorectification):
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return None
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pass
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'''
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if __name__=="__main__":
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path2=r"D:\LTOrth\Ortho"
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slcpath=r"D:\micro\WorkSpace\Ortho\Temporary\unpack\LT1B_MONO_KSC_STRIP2_007065_E100.8_N26.6_20230616_SLC_HH_L1A_0000135014"
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workspace_package_path=r"D:\micro\WorkSpace\Ortho\Temporary\package"
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Orthorectification = IndirectOrthorectification(os.path.join(path2,"config.yaml"))
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Orthorectification.IndirectOrthorectification(slcpath, workspace_package_path) # 改动1
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Orthorectification.test_PSTN() # 测试代码
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@ -525,6 +525,7 @@ class OrthoMain:
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Orth_Slc=[]
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in_dem_path = self.__in_processing_paras['DEM']
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meta_file_path = self.__in_processing_paras['META'] # .meta文件路径
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out_dem_path = self.__workspace_ResampledDEM_path
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dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif
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@ -538,7 +539,7 @@ class OrthoMain:
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path2 = env_str
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Orthorectification = IndirectOrthorectification(os.path.join(path2,"config.yaml"))
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Orthorectification.IndirectOrthorectification(self.__in_processing_paras["SLC"], self.__workspace_package_path) # 改动1
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# 2.3 输出结果
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Orthorectification.test_PSTN() # 测试代码
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# 3 处理RD
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in_slc_path=None
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@ -725,7 +726,7 @@ class OrthoMain:
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if __name__ == '__main__':
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DEBUG=False
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DEBUG=True
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if '-DEBUG' in sys.argv:
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DEBUG=True
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start = datetime.datetime.now()
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@ -52,7 +52,7 @@ imageinfo: # 影像信息
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"%Y-%m-%dT%H:%M:%S.%f"
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EndImageTime: # 影像中止时间
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NodePath:
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['level1Product','productInfo','sceneInfo', 'stop', 'timeUTC']
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['level1Product','productInfo','sceneInfo', 'stop', 'timeUTC']
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Format:
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"%Y-%m-%dT%H:%M:%S.%f"
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CenterImageTime: # 中心像元时间
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