SERC/Reflectance/Metadata/to-sensor_Zenith_Angle SERC/Reflectance/Metadata/to-sensor_Azimuth_Angle SERC/Reflectance/Metadata/Spectral_Data/Wavelength SERC/Reflectance/Metadata/Spectral_Data/FWHM SERC/Reflectance/Metadata/Logs/Solar_Zenith_Angle SERC/Reflectance/Metadata/Logs/Solar_Azimuth_Angle SERC/Reflectance/Metadata/Logs/Skyview_Processing_Log SERC/Reflectance/Metadata/Logs/Shadow_Processing_Log SERC/Reflectance/Metadata/Logs/ATCOR_input_file SERC/Reflectance/Metadata/Logs/ATCOR_Processing_Log SERC/Reflectance/Metadata/Flight_Trajectory/Flight_Time SERC/Reflectance/Metadata/Flight_Trajectory/Flight_Heading SERC/Reflectance/Metadata/Flight_Trajectory/Flight_Altitude SERC/Reflectance/Metadata/Coordinate_System/Proj4 SERC/Reflectance/Metadata/Coordinate_System/Map_Info
Python ffmpeg with hdf5 code#
SERC/Reflectance/Metadata/Coordinate_System/EPSG Code SERC/Reflectance/Metadata/Coordinate_System/Coordinate_System_String SERC/Reflectance/Metadata/Ancillary_Imagery/Water_Vapor_Column SERC/Reflectance/Metadata/Ancillary_Imagery/Visibility_Index_Map SERC/Reflectance/Metadata/Ancillary_Imagery/Smooth_Surface_Elevation SERC/Reflectance/Metadata/Ancillary_Imagery/Slope SERC/Reflectance/Metadata/Ancillary_Imagery/Sky_View_Factor SERC/Reflectance/Metadata/Ancillary_Imagery/Path_Length SERC/Reflectance/Metadata/Ancillary_Imagery/Illumination_Factor SERC/Reflectance/Metadata/Ancillary_Imagery/Haze_Cloud_Water_Map SERC/Reflectance/Metadata/Ancillary_Imagery/Dark_Dense_Vegetation_Classification SERC/Reflectance/Metadata/Ancillary_Imagery/Cast_Shadow SERC/Reflectance/Metadata/Ancillary_Imagery/Aspect The list_dataset function defined below displays all datasets stored in the hdf5 file and their locations within the hdf5 file: #list_dataset lists the names of datasets in an hdf5 file We can look inside the HDF5 dataset with the h5py visititems function. If the h5 file is stored in a different directory, make sure to include the relative path to that directory (In this example, the path is. '3.7.7 (default, Mar 23 2020, 17:31:31) \n'įirst let's import the required packages and set our display preferences so that plots are inline and plot warnings are off: import numpy as npį = h5py.File('file.h5','r') reads in an h5 file to the variable f. #Check that you are using the correct version of Python (should be 3.4+, otherwise gdal won't work) The gdal package is currently compatible with Python versions 3.4 and earlier (May 2017).įor this tutorial, we will use Python version 3.4. neon_aop_refl_hdf5_functions.py_.zip (5 KB) īefore we start coding, make sure you are using the correct version of Python.To complete this tutorial, you will use data available from the NEON 2017 Data Of an image (if you complete the optional extension). Apply a histogram stretch and adaptive equalization to improve the contrast.Of interest (if you complete the optional extension).
Python ffmpeg with hdf5 full#
Subset an hdf5 reflectance file from the full flightline to a smaller region.Plot a histogram of reflectance values to visualize the range and distribution.Extract and plot a single band of reflectance data.Read the data ignore value and scaling factor and apply these values to produce.Use the package h5py and the visititems functionality to read an HDF5 file.Import and use Python packages numpy, pandas, matplotlib, h5py, and gdal.Learning ObjectivesĪfter completing this tutorial, you will be able to: Please see NEON AOP Hyperspectral Data in HDF5 format with Python - Tiles.
Python ffmpeg with hdf5 how to#
If you are interested in learning how to do this for mosaic/tiled NEON AOP hyperspectral data, By the end of this tutorial, you will become We develop and practice skills and use several tools to manipulate and In this introductory tutorial, we discuss how to read NEON AOP hyperspectral flightlineĭata using Python.