72 lines
3.3 KiB
Python
72 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""
|
|
HyTools: Hyperspectral image processing library
|
|
Copyright (C) 2021 University of Wisconsin
|
|
|
|
Authors: Adam Chlus, Zhiwei Ye, Philip Townsend.
|
|
|
|
This program is free software: you can redistribute it and/or modify
|
|
it under the terms of the GNU General Public License as published by
|
|
the Free Software Foundation, version 3 of the License.
|
|
|
|
This program is distributed in the hope that it will be useful,
|
|
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
GNU General Public License for more details.
|
|
|
|
You should have received a copy of the GNU General Public License
|
|
along with this program. If not, see <https://www.gnu.org/licenses/>.
|
|
|
|
NEON AOP HDF opener
|
|
"""
|
|
import h5py
|
|
import numpy as np
|
|
|
|
|
|
def open_neon(hy_obj, no_data = -9999):
|
|
"""Load and parse NEON formated HDF image into a HyTools file object.
|
|
|
|
Args:
|
|
src_file (str): pathname of input HDF file.
|
|
no_data (float, optional): No data value. Defaults to -9999.
|
|
|
|
Returns:
|
|
HyTools file object: Populated HyTools file object.
|
|
|
|
"""
|
|
|
|
hdf_obj = h5py.File(hy_obj.file_name,'r')
|
|
hy_obj.base_key = list(hdf_obj.keys())[0]
|
|
metadata = hdf_obj[hy_obj.base_key]["Reflectance"]["Metadata"]
|
|
data = hdf_obj[hy_obj.base_key]["Reflectance"]["Reflectance_Data"]
|
|
|
|
hy_obj.projection = metadata['Coordinate_System']['Coordinate_System_String'][()].decode("utf-8")
|
|
hy_obj.map_info = metadata['Coordinate_System']['Map_Info'][()].decode("utf-8").split(',')
|
|
hy_obj.transform = (float(hy_obj.map_info [3]),float(hy_obj.map_info [1]),0,float(hy_obj.map_info [4]),0,-float(hy_obj.map_info [2]))
|
|
hy_obj.fwhm = metadata['Spectral_Data']['FWHM'][()]
|
|
hy_obj.wavelengths = metadata['Spectral_Data']['Wavelength'][()]
|
|
hy_obj.wavelength_units = metadata['Spectral_Data']['Wavelength'].attrs['Units']
|
|
hy_obj.lines = data.shape[0]
|
|
hy_obj.columns = data.shape[1]
|
|
hy_obj.bands = data.shape[2]
|
|
hy_obj.bad_bands = np.array([False for band in range(hy_obj.bands)])
|
|
hy_obj.no_data = no_data
|
|
hy_obj.anc_path = {'path_length': ['Ancillary_Imagery','Path_Length'],
|
|
'sensor_az': ['to-sensor_Azimuth_Angle'],
|
|
'sensor_zn': ['to-sensor_Zenith_Angle'],
|
|
'solar_az': ['Logs','Solar_Azimuth_Angle'],
|
|
'solar_zn': ['Logs','Solar_Zenith_Angle'],
|
|
'slope': ['Ancillary_Imagery','Slope'],
|
|
'aspect':['Ancillary_Imagery','Aspect'],
|
|
'aod': ['Ancillary_Imagery','Aerosol_Optical_Depth'],
|
|
'sky_view': ['Ancillary_Imagery','Sky_View_Factor'],
|
|
'illum_factor': ['Ancillary_Imagery','Illumination_Factor'],
|
|
'elevation;': ['Ancillary_Imagery','Smooth_Surface_Elevation'],
|
|
'cast_shadow': ['Ancillary_Imagery','Cast_Shadow'],
|
|
'dense_veg': ['Ancillary_Imagery','Dark_Dense_Vegetation_Classification'],
|
|
'visibility_index': ['Ancillary_Imagery','Visibility_Index_Map'],
|
|
'haze_water_cloud': ['Ancillary_Imagery','Haze_Water_Cloud_Map'],
|
|
'water_vapor': ['Ancillary_Imagery','Water_Vapor_Column']}
|
|
|
|
return hy_obj
|