Category Archives: Hyperspectral Imaging

chatGPT, colab, Hyperspectral Imaging, python

Visualising Satellite data using Google Colab

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Having spent a few hours reading documentation and having an ongoing conversation with chatGPT, I’m getting the hang of the hdf5 file structure and can now visualise some multispectral data in Google Colab:

from google.colab import drive
drive.mount('/content/drive')

import h5py
import numpy as np
import matplotlib.pyplot as plt

# Get the HDF5 file from your Google Drive
file_id = '/content/drive/MyDrive/DATA/file_name.he5'

with h5py.File(file_id, "r") as f:
# List all groups
print("Keys: %s" % f.keys())
a_group_key = list(f.keys())[0]

# Get the data
data = list(f[a_group_key])

#This gives us some idea about the groups/keys in the hdf file and some idea about the datasets contained therein - but will become more detailed as we go along

# Open the HDF5 file
with h5py.File(file_id, 'r') as f:
# Open the data field
#currently this is hard-coded as I know from hdfView that this is the path I want to look at - but really we want to find this programmatically.
data_field = f['/path_to/Data Fields/Cube']
# Print the shape of the data field
print(f'Shape: {data_field.attrs}')
print(f'Shape: {data_field.dtype}')
print(f'Shape: {data_field.shape}')
#This gives us some idea about the data cube we are examining - such as its attributes, data type and shape (typically rows and columns) - it'll print them to output

# Open the HDF5 file
with h5py.File(file_id, 'r') as f:
# Open the data field
data_field = f['/path_to/Data Fields/Cube']
# Get the data and reshape it to 2D
data = np.array(data_field[:]).reshape(data_field.shape[0], data_field.shape[1])
# Scale the data to fit within an 800x800 pixels
data = np.uint8(np.interp(data, (data.min(), data.max()), (0, 255)))
# Create a figure with the specified size
fig = plt.figure(figsize=(8, 8))
# Add the data to the figure
plt.imshow(data, cmap='viridis')
# Display the figure
plt.show()

Satellite data plotted with viridis colourmap

Next steps involve developing a way of iteratively traversing the hdf5 directory structure, so that I can identify relevant data fields within the file – they’re not explicitly identified as ‘image files’. This can be done using h5py functions. Another thing to explore is GDAL: once I’ve identified the correct data in geolocation fields, it should become possible to output geotiffs or UE-friendly png files with geolocation metadata.

Here’s some other matplotlib colourmaps applied to the same dataset.

It’s all pretty crude at this point – just figuring out how this stuff might work.

chatGPT, colab, Hyperspectral Imaging, python

ChatGPT, Google Colab and Hyperspectral Data

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It occurs to me that, rather than going to all the hassle of figuring out how to write all the code for colab myself, that I can use my chatGPT assistant to do it for me:

Q: write a script for google colab to extract images from an hdf5 file located upon my google drive

Chat GPT:

Here is an example script that demonstrates how to extract images from an HDF5 file located on your Google Drive using the h5py library in Python, and the PyDrive library to interact with Google Drive:

# Install the PyDrive library
!pip install -U PyDrive

# Import the necessary libraries
import h5py
import numpy as np
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

# Authenticate and create the PyDrive client
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

# Get the HDF5 file from your Google Drive
file_id = '<YOUR FILE ID>'
downloaded = drive.CreateFile({'id': file_id})
downloaded.GetContentFile('hyperspectral_image_data.hdf5')

# Open the HDF5 file
with h5py.File('hyperspectral_image_data.hdf5', 'r') as hdf5_file:
# Access the dataset containing the images
images = hdf5_file['images']
# Extract an image by its index
image = np.array(images[0])
print(image)

Make sure to replace <YOUR FILE ID> with the actual file ID of the HDF5 file on your Google Drive. You can find the file ID by opening the file in Google Drive and looking at the URL – the file ID is the string of letters and numbers after the last forward slash.

You can also use the gdal_translate command with the PyDrive library as well, just add the following lines of code to download the file and then use gdal_translate command to convert to tiff or geotiff

# Get the HDF5 file from your Google Drive
file_id = '<YOUR FILE ID>'
downloaded = drive.CreateFile({'id': file_id})
downloaded.GetContentFile('hyperspectral_image_data.hdf ')#Use gdal_translate
!gdal_translate -of GTiff HDF5:"hyperspectral_image_data.hdf5"://dataset output.tif

You can then use any image processing library to open and read the image.

Lots of interesting things to unpack in this advice – and lots of errors as I expect to debug the code.

So, as a first step, let’s see if we can just get access to the file and some basic metadata.

# Install the PyDrive library
!pip install -U PyDrive
# Import the necessary libraries
import h5py
import numpy as np
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
# Authenticate and create the PyDrive client
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
# Get the HDF5 file from your Google Drive
file_id = '/content/drive/MyDrive/DATA/XXXXXXX.he5'
with h5py.File(file_id, "r") as f:
    # List all groups
    print("Keys: %s" % f.keys())
    a_group_key = list(f.keys())[0]
    # Get the data
    data = list(f[a_group_key])

 

Success! We can see the HDF keys returned below:

Keys: <KeysViewHDF5 ['HDFEOS', 'HDFEOS INFORMATION', 'Info', 'KDP_AUX']>

These match the top-level directory structure revealed by HDFView:

This is the first step in identifying the file contents, before we can drill down into the file structure to identify a whole bunch of parameters about the datacube that we can do interesting things with 🙂

chatGPT, Hyperspectral Imaging

Working with ChatGPT & Hyperspectral Data

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Thus far I have been playing around with HDFView to extract hdf5 image data – but I am very interested in working with advanced machine learning systems and developing processes to interface them with Unreal Engine and on-line data sources. It seems the most tractable way to do this is through the use of a Python API – which is pretty much the lingua franca of scientific computing.

As I am a neophyte with using hyperspectral image data, let’s use ChatGPT to find out what might be an ‘easy’ or ‘best’ way to access image data within an hdf5 file:

Q: what is the best way to extract images from an hdf5 file containing hyperspectral image data?

ChatGPT:

One way to extract images from an HDF5 file containing hyperspectral image data is to use the h5py library in Python. This library allows you to open the HDF5 file and access the data stored within it, including the images. You can then use the numpy library to manipulate and extract the images as needed. Here is an example of how you might extract an image from an HDF5 file using h5py:

import h5py
import numpy as np# Open the HDF5 file
with h5py.File(‘hyperspectral_image_data.hdf5’, ‘r’) as hdf5_file:
# Access the dataset containing the images
images = hdf5_file[‘images’]
# Extract an image by its index
image = np.array(images[0])
print(image)

Another way is to use the gdal library in python, specifically with the gdal_translate utility, which can convert HDF5 to other raster formats like tiff or geotiff, and then use any image processing library to open and read the image.

gdal_translate -of GTiff HDF5:"file.hdf5"://dataset output.tif

In this way you will be able to use any library of your preference to read and extract the images.

I have no idea currently whether this will work, so lets look up the relevant documentation:

Both look promising – and has saved me a heap of time looking for ways to do it!

The first outputs images as a numPy array – meaning that we can examine/export each image by its index – which would be useful for selecting for certain  λ (wavelength)) values and conducting operations upon them.

The second uses GDAL (Geospatial Data Abstraction Library), which provides powerful utilities for the translation of geospatial metadata – enabling correct geolocation of the hyperspectral image data, for instance.

So perhaps a combination of both will be useful as we proceed.

But of course, any code generated by chatGPT or OpenAI Codex or other AI assistants must be taken with several grains of salt. For instance – a recent study by MIT shows that users may write more insecure code when working with an AI code assistant (https://doi.org/10.48550/arXiv.2211.03622). Perhaps there are whole API calls and phrases that are hallucinated? I simply don’t know at this stage.

So – my next step will be to fire up a python environment – probably Google Colab or Anaconda and see what happens.

A nice overview of Codex here:

OpenAI Codex Live Demo

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discussion, Hyperspectral Imaging, Research

Technical Webinar: Can satellites monitor crop and pasture quality across Australia?

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From the SmartSat CRC Newsletter:

This Technical Presentation hosted by Ha Thanh Nguyen, Research Scientist in Digital Interactions (Agriculture & Food) at CSIRO explores the outcomes of SmartSat Project P3.25: Can satellites monitor crop and pasture quality across Australia?

Knowledge of crop and pasture quality can provide the industry with insights to assist with the grazing management of pastures and input management decisions for crops. Handheld and lab-based spectroscopy have been extensively employed to monitor quality-based plant attributes. The methods employed are time consuming and expensive to implement and do not provide the industry with insights into the temporal trends of the critical variables. High resolution and frequent return time can overcome numerous deficiencies affecting equivalent visible IR and SWIR platforms, that limit the ability to create a viable product around crop and pasture quality. This project conducted a feasibility analysis capitalising on existing and planned satellite missions, including the Aquawatch satellites and precursors to test development of new high frequency products for crop and pasture quality across the Australian landscape. This project is led by Dr Roger Lawes, Principal Research Scientist at CSIRO Agriculture Flagship and includes participants from CSIRO and the Grains Research and Development Corporation. For more information visit https://smartsatcrc.com/research-prog…

Hyperspectral Imaging

Scope: Hyperspectral Imaging, Earth Model, Unreal Engine

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Our initial scope is to examine how we can can use hyperspectral satellite data within Unreal Engine (UE).

Hyperspectral Imaging

Wikipedia entry for Hyperspectral Imaging:

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum.[1] The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.[2][3] There are three general branches of spectral imagers. There are push broom scanners and the related whisk broom scanners (spatial scanning), which read images over time, band sequential scanners (spectral scanning), which acquire images of an area at different wavelengths, and snapshot hyperspectral imaging, which uses a staring array to generate an image in an instant.

Whereas the human eye sees color of visible light in mostly three bands (long wavelengths – perceived as red, medium wavelengths – perceived as green, and short wavelengths – perceived as blue), spectral imaging divides the spectrum into many more bands. This technique of dividing images into bands can be extended beyond the visible. In hyperspectral imaging, the recorded spectra have fine wavelength resolution and cover a wide range of wavelengths. Hyperspectral imaging measures continuous spectral bands, as opposed to multiband imaging which measures spaced spectral bands.[4]

Hyperspectral imaging satellites are equipped with special imaging sensors that collect data as a set of raster images, typically visualised as a stack, which each layer representing a discrete wavelength captured by the sensor array.

HyperspectralCube.jpg
By Dr. Nicholas M. Short, Sr. - NASA Link

This represents a 3-dimensional hyperspectral data cube, consisting of x,y coordinates that correspond to Earth-coordinates within a defined geodetic reference system, and the λ coordinate which corresponds to spectral wavelength.

Earth Model

An Earth Model means precisely that – a model of the Earth!

More specifically, the Earth is understood as a highly complex three-dimensional shape. It can be simply represented as a sphere or more accurately as an oblate spheroid, representing the diametric differences between equatorial and polar diameters caused by the rotation of the planet.

The scientific study of the shape of the Earth is called Geodesy. Arising from this is the World Geodetic System, about which Wikipedia notes:

The World Geodetic System (WGS) is a standard used in cartography, geodesy, and satellite navigation including GPS. The current version, WGS 84, defines an Earth-centered, Earth-fixed coordinate system and a geodetic datum, and also describes the associated Earth Gravitational Model (EGM) and World Magnetic Model (WMM). The standard is published and maintained by the United States National Geospatial-Intelligence Agency.[1]

Most relevant to creating a digital model of the Earth is the Earth-centred, Earth-fixed coordinate system (or ECEF):

The Earth-centered, Earth-fixed coordinate system (acronym ECEF), also known as the geocentric coordinate system, is a cartesian spatial reference system that represents locations in the vicinity of the Earth (including its surface, interior, atmosphere, and surrounding outer space) as X, Y, and Z measurements from its center of mass.[1][2] Its most common use is in tracking the orbits of satellites and in satellite navigation systems for measuring locations on the surface of the Earth, but it is also used in applications such as tracking crustal motion.

The distance from a given point of interest to the center of Earth is called the geocentric distance, R = (X2 + Y2 + Z2)0.5, which is a generalization of the geocentric radius, R0, not restricted to points on the reference ellipsoid surface. The geocentric altitude is a type of altitude defined as the difference between the two aforementioned quantities: h′ = R − R0;[3] it is not to be confused for the geodetic altitude.

Conversions between ECEF and geodetic coordinates (latitude and longitude) are discussed at geographic coordinate conversion.

Unreal Engine

Unreal Engine 5.1 provides a comprehensive system for managing ECEF georeferenced data, including ‘flat’ and ’round’ planet projections:

https://docs.unrealengine.com/5.1/en-US/georeferencing-a-level-in-unreal-engine/

In addition to this, there are a variety of plugins available for managing georeferenced data.

For this research we are evaluating two initial approaches:

Cesium for Unreal: https://cesium.com/platform/cesium-for-unreal/

Landscaping Plugin:  https://landscaping.ludicdrive.com

More soon.