1. 使用scipy.interpolate.interp1d重采样能量曲线到影像的波段；

2. 修改：使用spectral.envi.read_envi_header读取envi头文件；

1.1radiance_calibration_300tc.py

@@ -10,6 +10,8 @@ from radiance_calibration_ui import Ui_MainWindow
 from library.multithread import Worker
 import sys, traceback
 from osgeo import gdal
+from scipy.interpolate import interp1d
+import spectral
 
 from PyQt5.QtWidgets import QMainWindow, QFileDialog, QApplication
 from PyQt5.QtCore import Qt
@@ -142,8 +144,16 @@ class RadianceCalibration():
         return os.path.splitext(file_path)[0] + ".bip.hdr"
 
     def write_fields_to_hdrfile(self, fields, hdr_file):
+        header_tmp = spectral.envi.read_envi_header(hdr_file)
+
         with open(hdr_file, "a", encoding='utf-8') as f:
             for key in fields.keys():
+                if key in header_tmp or key == "description":
+                    continue
+
+                if type(fields[key]) == list:
+                    f.write(key + " = {" + ", ".join(fields[key]) + "}\n")
+                else:
                     f.write(key + " = " + fields[key] + "\n")
 
     def process_hdr(self, hdr_file_path, envi_header_dict):
@@ -193,9 +203,23 @@ class RadianceCalibration():
 
             # 读取影像
             img_proj, img_geotrans, self.img_data = self.read_img(self.dn_file_path)
-            dn_hdr = self.get_envi_header_dict(self.get_hdr_filename(self.dn_file_path))
+            # dn_hdr = self.get_envi_header_dict(self.get_hdr_filename(self.dn_file_path))
+            dn_hdr = spectral.envi.read_envi_header(self.get_hdr_filename(self.dn_file_path))
             img_dark_proj, img_dark_geotrans, self.img_dark_data = self.read_img(self.dark_file_path)
-            dark_hdr = self.get_envi_header_dict(self.get_hdr_filename(self.dark_file_path))
+            # dark_hdr = self.get_envi_header_dict(self.get_hdr_filename(self.dark_file_path))
+            dark_hdr = spectral.envi.read_envi_header(self.get_hdr_filename(self.dark_file_path))
+
+            # 重采样
+            f = interp1d(self.asd_radiance[:, 0], self.asd_radiance[:, 1])
+            # wave_destination = dn_hdr["wavelength"].removeprefix("{").removesuffix("}").split(",")
+            asd_radiance_interpolated = [f(float(i)) for i in dn_hdr["wavelength"]]
+            asd_radiance_interpolated2 = np.array(asd_radiance_interpolated)
+
+            notuse = np.zeros((asd_radiance_interpolated2.shape[0], 2))
+            notuse[:, 0] = np.array([float(i) for i in dn_hdr["wavelength"]])
+            notuse[:, 1] =asd_radiance_interpolated2
+            np.savetxt(os.path.join(self.out_file_path, "asd_radiance_interpolated.txt"), notuse)
+
 
             # 将影像所有行平均，得到一行（帧）影像
             img_data_ave = np.mean(self.img_data, axis=1)
@@ -203,12 +227,11 @@ class RadianceCalibration():
 
             # 去除暗电流
             img_data_ave_rmdark = img_data_ave - img_dark_data_ave
-            img_dark_data_ave = img_dark_data_ave + 50
 
             gain = np.empty((img_data_ave_rmdark.shape[0], 1, img_data_ave_rmdark.shape[1]))
             offset = np.empty((img_data_ave_rmdark.shape[0], 1, img_data_ave_rmdark.shape[1]))
             for i in range(gain.shape[2]):
-                gain[:, 0, i] = self.asd_radiance[:, 1] / img_data_ave_rmdark[:, i]
+                gain[:, 0, i] = asd_radiance_interpolated2 / img_data_ave_rmdark[:, i]
                 offset[:, 0, i] = img_dark_data_ave[:, i]
 
             gain_name = os.path.join(self.out_file_path, "gain.bip")