syncID: 61ad1fc43ddd45b49cad1bca48656bbe title: "NEON AOP Hyperspectral Data in HDF5 format with Python - Tiles" description: "Learn how to read NEON AOP hyperspectral flightline data using Python and develop skills to manipulate and visualize spectral data." dateCreated: 2018-07-04 authors: Bridget Hass contributors: estimatedTime: packagesLibraries: numpy, h5py, gdal, matplotlib.pyplot topics: hyperspectral-remote-sensing, HDF5, remote-sensing languagesTool: python dataProduct: NEON.DP3.30006, NEON.DP3.30008 code1: Python/remote-sensing/hyperspectral-data/Intro_NEON_AOP_HDF5_Reflectance_Tiles_py.ipynb tutorialSeries: intro-hsi-py-series
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.
After completing this tutorial, you will be able to:
numpy, pandas, matplotlib, h5py, and gdal.h5py and the visititems functionality to read an HDF5 file
and view data attributes.{% include/dataSubsets/_data_DI18.html %}
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.
For more information on spectral remote sensing watch this video.
Before we start coding, make sure you are using the correct version of Python. As of July 2018, the gdal package is compatible with Python versions 3.5 and earlier. For these lessons we will use Python version 3.5.
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#Check that you are using the correct version of Python (should be 3.5 for these tutorials)
import sys
sys.version
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First let's import the required packages:
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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:
In [3]:
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
f = h5py.File('file.h5','r') reads in an h5 file to the variable f.
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:
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help(h5py)
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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.
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f = h5py.File('../../data/NEON_D02_SERC_DP3_368000_4306000_reflectance.h5','r')
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#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)
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_DataSERC/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:
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#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)
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#to see what the visititems methods does, type ? at the end:
f.visititems?
In [10]:
f.visititems(ls_dataset)
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:
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serc_refl = f['SERC']['Reflectance']
print(serc_refl)
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:
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serc_reflArray = serc_refl['Reflectance_Data']
print(serc_reflArray)
We can extract the size of this reflectance array that we extracted using the shape method:
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refl_shape = serc_reflArray.shape
print('SERC Reflectance Data Dimensions:',refl_shape)
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.
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:
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#define the wavelengths variable
wavelengths = serc_refl['Metadata']['Spectral_Data']['Wavelength']
#View wavelength information and values
print('wavelengths:',wavelengths)
We can then use numpy (imported as np) to see the minimum and maximum wavelength values:
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# Display min & max wavelengths
print('min wavelength:', np.amin(wavelengths),'nm')
print('max wavelength:', np.amax(wavelengths),'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).
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#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')
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:
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serc_mapInfo = serc_refl['Metadata']['Coordinate_System']['Map_Info']
print('SERC Map Info:',serc_mapInfo.value)
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 zoneN - UTM hemisphere (North for all NEON sites)WGS-84 - reference ellipoidThe 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:
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#First convert mapInfo to a string
mapInfo_string = str(serc_mapInfo.value) #convert to string
#see what the split method does
mapInfo_string.split?
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#split the strings using the separator ","
mapInfo_split = mapInfo_string.split(",")
print(mapInfo_split)
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:
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#Extract the resolution & convert to floating decimal number
res = float(mapInfo_split[5]),float(mapInfo_split[6])
print('Resolution:',res)
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#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.
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#Define extent as a tuple:
serc_ext = (xMin, xMax, yMin, yMax)
print('serc_ext:',serc_ext)
print('serc_ext type:',type(serc_ext))
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.
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b56 = serc_reflArray[:,:,55].astype(float)
print('b56 type:',type(b56))
print('b56 shape:',b56.shape)
print('Band 56 Reflectance:\n',b56)
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.
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).
We can extract and apply the Data_Ignore_Value and Scale_Factor as follows:
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#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)
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.
In [25]:
serc_plot = plt.imshow(b56,extent=serc_ext,cmap='Greys')
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.
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plt.hist(b56[~np.isnan(b56)],50); #50 signifies the # of bins
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:
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serc_plot = plt.imshow(b56,extent=serc_ext,cmap='Greys',clim=(0,0.4))
plt.title('SERC Band 56 Reflectance');
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.
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.
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:
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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=(0,100,1))
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