Tutorial

NEON AOP Hyperspectral Data in HDF5 format with Python - Tiled Data

Authors: Bridget Hass

Last Updated: Apr 6, 2021

In this introductory tutorial, we discuss how to read NEON AOP hyperspectral flightline data using Python. We develop and practice skills and use several tools to manipulate and visualize the spectral data. By the end of this tutorial, you will become familiar with the Python syntax.

If you are interested in learning how to do this for flightline NEON AOP hyperspectral data, please see NEON AOP Hyperspectral Data in HDF5 format with Python - Flightlines.

Learning Objectives

After completing this tutorial, you will be able to:

  • Import and use Python packages numpy, pandas, matplotlib, h5py, and gdal.
  • Use the package h5py and the visititems functionality to read an HDF5 file and view data attributes.
  • Read the data ignore value and scaling factor and apply these values to produce a cleaned reflectance array.
  • Extract and plot a single band of reflectance data
  • Plot a histogram of reflectance values to visualize the range and distribution of values.
  • Subset an hdf5 reflectance file from the full flightline to a smaller region of interest (if you complete the optional extension).
  • Apply a histogram stretch and adaptive equalization to improve the contrast of an image (if you complete the optional extension) .

Install Python Packages

  • numpy
  • pandas
  • gdal
  • matplotlib
  • h5py

Download Data

To complete this tutorial, you will use data available from the NEON 2017 Data Institute.

This tutorial uses the following files:

Download Dataset

The LiDAR and imagery data used to create this raster teaching data subset were collected over the National Ecological Observatory Network's field sites and processed at NEON headquarters. The entire dataset can be accessed on the NEON data portal.

Hyperspectral remote sensing data is a useful tool for measuring changes to our environment at the Earth’s surface. In this tutorial we explore how to extract information from a tile (1000m x 1000m x 426 bands) of NEON AOP orthorectified surface reflectance data, stored in hdf5 format. For more information on this data product, refer to the NEON Data Product Catalog.

Mapping the Invisible: Introduction to Spectral Remote Sensing

For more information on spectral remote sensing watch this video.

Set up

First let's import the required packages:

import numpy as np
import h5py
import gdal, osr, os
import matplotlib.pyplot as plt

Next, set display preferences so that plots are inline (meaning any images you output from your code will show up below the cell in the notebook) and turn off plot warnings:

%matplotlib inline
import warnings
warnings.filterwarnings('ignore')

Read in hdf5

f = h5py.File('file.h5','r') reads in an h5 file to the variable f.

Using the help

We will be using a number of built-in and user-defined functions and methods throughout the tutorial. If you are uncertain what a certain function does, or how to call it, you can type help() or type a ? at the end of the function or method and run the cell (either select Cell > Run Cells or Shift Enter with your cursor in the cell you want to run). The ? will pop up a window at the bottom of the notebook displaying the function's docstrings, which includes information about the function and usage. We encourage you to use help and ? throughout the tutorial as you come across functions you are unfamiliar with. Let's try this out with h5py.File:

help(h5py)

Help on package h5py:

NAME
    h5py

DESCRIPTION
    This is the h5py package, a Python interface to the HDF5
    scientific data format.

PACKAGE CONTENTS
    _conv
    _errors
    _hl (package)
    _objects
    _proxy
    defs
    h5
    h5a
    h5ac
    h5d
    h5ds
    h5f
    h5fd
    h5g
    h5i
    h5l
    h5o
    h5p
    h5pl
    h5py_warnings
    h5r
    h5s
    h5t
    h5z
    highlevel
    ipy_completer
    tests (package)
    utils
    version

SUBMODULES
    filters

FUNCTIONS
    check_enum_dtype(...)
        If the dtype represents an HDF5 enumerated type, returns the dictionary
        mapping string names to integer values.
        
        Returns None if the dtype does not represent an HDF5 enumerated type.
    
    check_ref_dtype(...)
        If the dtype represents an HDF5 reference type, returns the reference
        class (either Reference or RegionReference).
        
        Returns None if the dtype does not represent an HDF5 reference type.
    
    check_string_dtype(...)
        If the dtype represents an HDF5 string, returns a string_info object.
        
        The returned string_info object holds the encoding and the length.
        The encoding can only be 'utf-8' or 'ascii'. The length may be None
        for a variable-length string, or a fixed length in bytes.
        
        Returns None if the dtype does not represent an HDF5 string.
    
    check_vlen_dtype(...)
        If the dtype represents an HDF5 vlen, returns the Python base class.
        
        Returns None if the dtype does not represent an HDF5 vlen.
    
    enable_ipython_completer()
        Call this from an interactive IPython session to enable tab-completion
        of group and attribute names.
    
    enum_dtype(...)
        Create a NumPy representation of an HDF5 enumerated type
        
        *values_dict* maps string names to integer values. *basetype* is an
        appropriate integer base dtype large enough to hold the possible options.
    
    get_config(...)
        () => H5PYConfig
        
        Get a reference to the global library configuration object.
    
    get_enum = py_get_enum(...)
        (DTYPE dt_in) => DICT
        
        Deprecated; use check_dtype() instead.
    
    get_vlen = py_get_vlen(...)
        (OBJECT dt_in) => TYPE
        
        Deprecated; use check_vlen_dtype() instead.
    
    new_enum = py_new_enum(...)
        (DTYPE dt_in, DICT enum_vals) => DTYPE
        
        Deprecated; use enum_dtype() instead.
    
    new_vlen = py_new_vlen(...)
        (OBJECT kind) => DTYPE
        
        Deprecated; use vlen_dtype() instead.
    
    run_tests(args='')
        Run tests with pytest and returns the exit status as an int.
    
    string_dtype(...)
        Make a numpy dtype for HDF5 strings
        
        encoding may be 'utf-8' or 'ascii'.
        
        length may be an integer for a fixed length string dtype, or None for
        variable length strings. String lengths for HDF5 are counted in bytes,
        not unicode code points.
        
        For variable length strings, the data should be passed as Python str objects
        (unicode in Python 2) if the encoding is 'utf-8', and bytes if it is 'ascii'.
        For fixed length strings, the data should be numpy fixed length *bytes*
        arrays, regardless of the encoding. Fixed length unicode data is not
        supported.
    
    vlen_dtype(...)
        Make a numpy dtype for an HDF5 variable-length datatype
        
        For variable-length string dtypes, use :func:`string_dtype` instead.

DATA
    absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...
    highlevel = <h5py.h5py_warnings.ModuleWrapper object>
    ref_dtype = dtype('O')
    regionref_dtype = dtype('O')

VERSION
    2.10.0

FILE
    /opt/anaconda3/envs/py37/lib/python3.7/site-packages/h5py/__init__.py

h5py.File?

Now that we have an idea of how to use h5py to read in an h5 file, let's try it out. Note that if the h5 file is stored in a different directory than where you are running your notebook, you need to include the path (either relative or absolute) to the directory where that data file is stored. Use os.path.join to create the full path of the file.

# Note that you will need to update this filepath for your local machine
f = h5py.File('/Users/olearyd/Git/data/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5','r')

Explore NEON AOP HDF5 Reflectance Files

We can look inside the HDF5 dataset with the h5py visititems function. 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
def list_dataset(name,node):
    if isinstance(node, h5py.Dataset):
        print(name)

f.visititems(list_dataset)

SERC/Reflectance/Metadata/Ancillary_Imagery/Aerosol_Optical_Depth
SERC/Reflectance/Metadata/Ancillary_Imagery/Aspect
SERC/Reflectance/Metadata/Ancillary_Imagery/Cast_Shadow
SERC/Reflectance/Metadata/Ancillary_Imagery/Dark_Dense_Vegetation_Classification
SERC/Reflectance/Metadata/Ancillary_Imagery/Data_Selection_Index
SERC/Reflectance/Metadata/Ancillary_Imagery/Haze_Cloud_Water_Map
SERC/Reflectance/Metadata/Ancillary_Imagery/Illumination_Factor
SERC/Reflectance/Metadata/Ancillary_Imagery/Path_Length
SERC/Reflectance/Metadata/Ancillary_Imagery/Sky_View_Factor
SERC/Reflectance/Metadata/Ancillary_Imagery/Slope
SERC/Reflectance/Metadata/Ancillary_Imagery/Smooth_Surface_Elevation
SERC/Reflectance/Metadata/Ancillary_Imagery/Visibility_Index_Map
SERC/Reflectance/Metadata/Ancillary_Imagery/Water_Vapor_Column
SERC/Reflectance/Metadata/Ancillary_Imagery/Weather_Quality_Indicator
SERC/Reflectance/Metadata/Coordinate_System/Coordinate_System_String
SERC/Reflectance/Metadata/Coordinate_System/EPSG Code
SERC/Reflectance/Metadata/Coordinate_System/Map_Info
SERC/Reflectance/Metadata/Coordinate_System/Proj4
SERC/Reflectance/Metadata/Logs/160154/ATCOR_Input_file
SERC/Reflectance/Metadata/Logs/160154/ATCOR_Processing_Log
SERC/Reflectance/Metadata/Logs/160154/Shadow_Processing_Log
SERC/Reflectance/Metadata/Logs/160154/Skyview_Processing_Log
SERC/Reflectance/Metadata/Logs/160154/Solar_Azimuth_Angle
SERC/Reflectance/Metadata/Logs/160154/Solar_Zenith_Angle
SERC/Reflectance/Metadata/Logs/160742/ATCOR_Input_file
SERC/Reflectance/Metadata/Logs/160742/ATCOR_Processing_Log
SERC/Reflectance/Metadata/Logs/160742/Shadow_Processing_Log
SERC/Reflectance/Metadata/Logs/160742/Skyview_Processing_Log
SERC/Reflectance/Metadata/Logs/160742/Solar_Azimuth_Angle
SERC/Reflectance/Metadata/Logs/160742/Solar_Zenith_Angle
SERC/Reflectance/Metadata/Logs/161252/ATCOR_Input_file
SERC/Reflectance/Metadata/Logs/161252/ATCOR_Processing_Log
SERC/Reflectance/Metadata/Logs/161252/Shadow_Processing_Log
SERC/Reflectance/Metadata/Logs/161252/Skyview_Processing_Log
SERC/Reflectance/Metadata/Logs/161252/Solar_Azimuth_Angle
SERC/Reflectance/Metadata/Logs/161252/Solar_Zenith_Angle
SERC/Reflectance/Metadata/Spectral_Data/FWHM
SERC/Reflectance/Metadata/Spectral_Data/Wavelength
SERC/Reflectance/Metadata/to-sensor_azimuth_angle
SERC/Reflectance/Metadata/to-sensor_zenith_angle
SERC/Reflectance/Reflectance_Data

You can see that there is a lot of information stored inside this reflectance hdf5 file. Most of this information is metadata (data about the reflectance data), for example, this file stores input parameters used in the atmospheric correction. For this introductory lesson, we will only work with two of these datasets, the reflectance data (hyperspectral cube), and the corresponding geospatial information, stored in Metadata/Coordinate_System:

  • SERC/Reflectance/Reflectance_Data
  • SERC/Reflectance/Metadata/Coordinate_System/

We can also display the name, shape, and type of each of these datasets using the ls_dataset function defined below, which is also called with the visititems method:

#ls_dataset displays the name, shape, and type of datasets in hdf5 file
def ls_dataset(name,node):
    if isinstance(node, h5py.Dataset):
        print(node)

#to see what the visititems methods does, type ? at the end:
f.visititems?

f.visititems(ls_dataset)

<HDF5 dataset "Aerosol_Optical_Depth": shape (1000, 1000), type "<i2">
<HDF5 dataset "Aspect": shape (1000, 1000), type "<f4">
<HDF5 dataset "Cast_Shadow": shape (1000, 1000), type "|u1">
<HDF5 dataset "Dark_Dense_Vegetation_Classification": shape (1000, 1000), type "|u1">
<HDF5 dataset "Data_Selection_Index": shape (1000, 1000), type "<i4">
<HDF5 dataset "Haze_Cloud_Water_Map": shape (1000, 1000), type "|u1">
<HDF5 dataset "Illumination_Factor": shape (1000, 1000), type "|u1">
<HDF5 dataset "Path_Length": shape (1000, 1000), type "<f4">
<HDF5 dataset "Sky_View_Factor": shape (1000, 1000), type "|u1">
<HDF5 dataset "Slope": shape (1000, 1000), type "<f4">
<HDF5 dataset "Smooth_Surface_Elevation": shape (1000, 1000), type "<f4">
<HDF5 dataset "Visibility_Index_Map": shape (1000, 1000), type "|u1">
<HDF5 dataset "Water_Vapor_Column": shape (1000, 1000), type "<f4">
<HDF5 dataset "Weather_Quality_Indicator": shape (1000, 1000, 3), type "|u1">
<HDF5 dataset "Coordinate_System_String": shape (), type "|O">
<HDF5 dataset "EPSG Code": shape (), type "|O">
<HDF5 dataset "Map_Info": shape (), type "|O">
<HDF5 dataset "Proj4": shape (), type "|O">
<HDF5 dataset "ATCOR_Input_file": shape (), type "|O">
<HDF5 dataset "ATCOR_Processing_Log": shape (), type "|O">
<HDF5 dataset "Shadow_Processing_Log": shape (), type "|O">
<HDF5 dataset "Skyview_Processing_Log": shape (), type "|O">
<HDF5 dataset "Solar_Azimuth_Angle": shape (), type "<f4">
<HDF5 dataset "Solar_Zenith_Angle": shape (), type "<f4">
<HDF5 dataset "ATCOR_Input_file": shape (), type "|O">
<HDF5 dataset "ATCOR_Processing_Log": shape (), type "|O">
<HDF5 dataset "Shadow_Processing_Log": shape (), type "|O">
<HDF5 dataset "Skyview_Processing_Log": shape (), type "|O">
<HDF5 dataset "Solar_Azimuth_Angle": shape (), type "<f4">
<HDF5 dataset "Solar_Zenith_Angle": shape (), type "<f4">
<HDF5 dataset "ATCOR_Input_file": shape (), type "|O">
<HDF5 dataset "ATCOR_Processing_Log": shape (), type "|O">
<HDF5 dataset "Shadow_Processing_Log": shape (), type "|O">
<HDF5 dataset "Skyview_Processing_Log": shape (), type "|O">
<HDF5 dataset "Solar_Azimuth_Angle": shape (), type "<f4">
<HDF5 dataset "Solar_Zenith_Angle": shape (), type "<f4">
<HDF5 dataset "FWHM": shape (426,), type "<f4">
<HDF5 dataset "Wavelength": shape (426,), type "<f4">
<HDF5 dataset "to-sensor_azimuth_angle": shape (1000, 1000), type "<f4">
<HDF5 dataset "to-sensor_zenith_angle": shape (1000, 1000), type "<f4">
<HDF5 dataset "Reflectance_Data": shape (1000, 1000, 426), type "<i2">

Now that we can see the structure of the hdf5 file, let's take a look at some of the information that is stored inside. Let's start by extracting the reflectance data, which is nested under SERC/Reflectance/Reflectance_Data:

serc_refl = f['SERC']['Reflectance']
print(serc_refl)

<HDF5 group "/SERC/Reflectance" (2 members)>

The two members of the HDF5 group /SERC/Reflectance are Metadata and Reflectance_Data. Let's save the reflectance data as the variable serc_reflArray:

serc_reflArray = serc_refl['Reflectance_Data']
print(serc_reflArray)

<HDF5 dataset "Reflectance_Data": shape (1000, 1000, 426), type "<i2">

We can extract the size of this reflectance array that we extracted using the shape method:

refl_shape = serc_reflArray.shape
print('SERC Reflectance Data Dimensions:',refl_shape)

SERC Reflectance Data Dimensions: (1000, 1000, 426)

This 3-D shape (1000,1000,426) corresponds to (y,x,bands), where (x,y) are the dimensions of the reflectance array in pixels. Hyperspectral data sets are often called "cubes" to reflect this 3-dimensional shape.

A "cube" showing a hyperspectral data set. Source: National Ecological Observatory Network (NEON)

NEON hyperspectral data contain around 426 spectral bands, and when working with tiled data, the spatial dimensions are 1000 x 1000, where each pixel represents 1 meter. Now let's take a look at the wavelength values. First, we will extract wavelength information from the serc_refl variable that we created:

#define the wavelengths variable
wavelengths = serc_refl['Metadata']['Spectral_Data']['Wavelength']

#View wavelength information and values
print('wavelengths:',wavelengths)

wavelengths: <HDF5 dataset "Wavelength": shape (426,), type "<f4">

We can then use numpy (imported as np) to see the minimum and maximum wavelength values:

# Display min & max wavelengths
print('min wavelength:', np.amin(wavelengths),'nm')
print('max wavelength:', np.amax(wavelengths),'nm')

min wavelength: 383.5343 nm
max wavelength: 2511.8945 nm

Finally, we can determine the band widths (distance between center bands of two adjacent bands). Let's try this for the first two bands and the last two bands. Remember that Python uses 0-based indexing ([0] represents the first value in an array), and note that you can also use negative numbers to splice values from the end of an array ([-1] represents the last value in an array).

#show the band widths between the first 2 bands and last 2 bands 
print('band width between first 2 bands =',(wavelengths.value[1]-wavelengths.value[0]),'nm')
print('band width between last 2 bands =',(wavelengths.value[-1]-wavelengths.value[-2]),'nm')

band width between first 2 bands = 5.007904 nm
band width between last 2 bands = 5.0078125 nm

The center wavelengths recorded in this hyperspectral cube range from 383.66 - 2511.94 nm, and each band covers a range of ~5 nm. Now let's extract spatial information, which is stored under SERC/Reflectance/Metadata/Coordinate_System/Map_Info:

serc_mapInfo = serc_refl['Metadata']['Coordinate_System']['Map_Info']
print('SERC Map Info:',serc_mapInfo.value)

SERC Map Info: b'UTM,  1.000,  1.000,       368000.00,       4307000.0,       1.0000000,       1.0000000,  18,  North,  WGS-84,  units=Meters, 0'

Understanding the output:

Here we can spatial information about the reflectance data. Below is a break down of what each of these values means:

  • UTM - coordinate system (Universal Transverse Mercator)
  • 1.000, 1.000 -
  • 368000.000, 4307000.0 - UTM coordinates (meters) of the map origin, which refers to the upper-left corner of the image (xMin, yMax).
  • 1.0000000, 1.0000000 - pixel resolution (meters)
  • 18 - UTM zone
  • N - UTM hemisphere (North for all NEON sites)
  • WGS-84 - reference ellipoid

The letter b that appears before UTM signifies that the variable-length string data is stored in binary format when it is written to the hdf5 file. Don't worry about it for now, as we will convert the numerical data we need into floating point numbers. For more information on hdf5 strings read the h5py documentation.

Let's extract relevant information from the Map_Info metadata to define the spatial extent of this dataset. To do this, we can use the split method to break up this string into separate values:

#First convert mapInfo to a string
mapInfo_string = str(serc_mapInfo.value) #convert to string

#see what the split method does
mapInfo_string.split?

#split the strings using the separator "," 
mapInfo_split = mapInfo_string.split(",") 
print(mapInfo_split)

["b'UTM", '  1.000', '  1.000', '       368000.00', '       4307000.0', '       1.0000000', '       1.0000000', '  18', '  North', '  WGS-84', '  units=Meters', " 0'"]

Now we can extract the spatial information we need from the map info values, convert them to the appropriate data type (float) and store it in a way that will enable us to access and apply it later when we want to plot the data:

#Extract the resolution & convert to floating decimal number
res = float(mapInfo_split[5]),float(mapInfo_split[6])
print('Resolution:',res)

Resolution: (1.0, 1.0)

#Extract the upper left-hand corner coordinates from mapInfo
xMin = float(mapInfo_split[3]) 
yMax = float(mapInfo_split[4])

#Calculate the xMax and yMin values from the dimensions
xMax = xMin + (refl_shape[1]*res[0]) #xMax = left edge + (# of columns * x pixel resolution)
yMin = yMax - (refl_shape[0]*res[1]) #yMin = top edge - (# of rows * y pixel resolution)

Now we can define the spatial exten as the tuple (xMin, xMax, yMin, yMax). This is the format required for applying the spatial extent when plotting with matplotlib.pyplot.

#Define extent as a tuple:
serc_ext = (xMin, xMax, yMin, yMax)
print('serc_ext:',serc_ext)
print('serc_ext type:',type(serc_ext))

serc_ext: (368000.0, 369000.0, 4306000.0, 4307000.0)
serc_ext type: <class 'tuple'>

Extract a Single Band from Array

While it is useful to have all the data contained in a hyperspectral cube, it is difficult to visualize all this information at once. We can extract a single band (representing a ~5nm band, approximating a single wavelength) from the cube by using splicing as follows. Note that we have to cast the reflectance data into the type float. Recall that since Python indexing starts at 0 instead of 1, in order to extract band 56, we need to use the index 55.

b56 = serc_reflArray[:,:,55].astype(float)
print('b56 type:',type(b56))
print('b56 shape:',b56.shape)
print('Band 56 Reflectance:\n',b56)

b56 type: <class 'numpy.ndarray'>
b56 shape: (1000, 1000)
Band 56 Reflectance:
 [[ 254.  241.  250. ...  334.  313.  330.]
 [ 253.  260.  624. ...  281.  311.  291.]
 [ 262.  413. 1050. ...  295.  349.  280.]
 ...
 [ 281.  231.  292. ... 1168.  978.  916.]
 [ 240.  222.  189. ... 1654. 1728. 1694.]
 [ 319.  329.  317. ... 1176. 1466. 1582.]]

Here we can see that we extracted a 2-D array (1000 x 1000) of the scaled reflectance data corresponding to the wavelength band 56. Before we can use the data, we need to clean it up a little. We'll show how to do this below.

Scale factor and No Data Value

This array represents the scaled reflectance for band 56. Recall from exploring the HDF5 data in HDFViewer that NEON AOP reflectance data uses a Data_Ignore_Value of -9999 to represent missing data (often called NaN), and a reflectance Scale_Factor of 10000.0 in order to save disk space (can use lower precision this way).

Screenshot of the NEON HDF5 file format. Source: National Ecological Observatory Network

We can extract and apply the Data_Ignore_Value and Scale_Factor as follows:

#View and apply scale factor and data ignore value
scaleFactor = serc_reflArray.attrs['Scale_Factor']
noDataValue = serc_reflArray.attrs['Data_Ignore_Value']
print('Scale Factor:',scaleFactor)
print('Data Ignore Value:',noDataValue)

b56[b56==int(noDataValue)]=np.nan
b56 = b56/scaleFactor
print('Cleaned Band 56 Reflectance:\n',b56)

Scale Factor: 10000.0
Data Ignore Value: -9999.0
Cleaned Band 56 Reflectance:
 [[0.0254 0.0241 0.025  ... 0.0334 0.0313 0.033 ]
 [0.0253 0.026  0.0624 ... 0.0281 0.0311 0.0291]
 [0.0262 0.0413 0.105  ... 0.0295 0.0349 0.028 ]
 ...
 [0.0281 0.0231 0.0292 ... 0.1168 0.0978 0.0916]
 [0.024  0.0222 0.0189 ... 0.1654 0.1728 0.1694]
 [0.0319 0.0329 0.0317 ... 0.1176 0.1466 0.1582]]

Plot single reflectance band

Now we can plot this band using the Python package matplotlib.pyplot, which we imported at the beginning of the lesson as plt. Note that the default colormap is jet unless otherwise specified. You can explore using different colormaps on your own; see the mapplotlib colormaps for for other options.

serc_plot = plt.imshow(b56,extent=serc_ext,cmap='Greys')

png

We can see that this image looks pretty washed out. To see why this is, it helps to look at the range and distribution of reflectance values that we are plotting. We can do this by making a histogram.

Plot histogram

We can plot a histogram using the matplotlib.pyplot.hist function. Note that this function won't work if there are any NaN values, so we can ensure we are only plotting the real data values using the call below. You can also specify the # of bins you want to divide the data into.

plt.hist(b56[~np.isnan(b56)],50); #50 signifies the # of bins

png

We can see that most of the reflectance values are < 0.4. In order to show more contrast in the image, we can adjust the colorlimit (clim) to 0-0.4:

serc_plot = plt.imshow(b56,extent=serc_ext,cmap='Greys',clim=(0,0.4)) 
plt.title('SERC Band 56 Reflectance');

png

Here you can see that adjusting the colorlimit displays features (eg. roads, buildings) much better than when we set the colormap limits to the entire range of reflectance values.

Extension: Basic Image Processing -- Contrast Stretch & Histogram Equalization

We can also try out some basic image processing to better visualize the reflectance data using the ski-image package.

Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Stretching the histogram can improve the contrast of a displayed image, as we will show how to do below.

Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Stretching the histogram can improve the contrast of a displayed image, as we will show how to do below. Source: Wikipedia - Public Domain

The following tutorial section is adapted from skikit-image's tutorial Histogram Equalization.

Below we demonstrate a widget to interactively display different linear contrast stretches:

Explore the contrast stretch feature interactively using IPython widgets:

from skimage import exposure
from IPython.html.widgets import *

def linearStretch(percent):
    pLow, pHigh = np.percentile(b56[~np.isnan(b56)], (percent,100-percent))
    img_rescale = exposure.rescale_intensity(b56, in_range=(pLow,pHigh))
    plt.imshow(img_rescale,extent=serc_ext,cmap='gist_earth') 
    #cbar = plt.colorbar(); cbar.set_label('Reflectance')
    plt.title('SERC Band 56 \n Linear ' + str(percent) + '% Contrast Stretch'); 
    ax = plt.gca()
    ax.ticklabel_format(useOffset=False, style='plain') #do not use scientific notation #
    rotatexlabels = plt.setp(ax.get_xticklabels(),rotation=90) #rotate x tick labels 90 degree
    
interact(linearStretch,percent=(0,50,1))

png

<function __main__.linearStretch(percent)>

Get Lesson Code

Intro_NEON_AOP_HDF5_Reflectance_Tiles_py.ipynb

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