Active Image Alignment

In most use cases, each band of a multispectral capture must be aligned with the other bands in order to create meaningful data. In this tutorial, we show how to align the band to each other using open source OpenCV utilities.

Image alignment allows the combination of images into true-color (RGB) and false color (such as CIR) composites, useful for scouting using single images as well as for display and management uses. In addition to composite images, alignment allows the calculation of pixel-accurate indices such as NDVI or NDRE at the single image level which can be very useful for applications like plant counting and coverage estimations, where mosaicing artifacts may otherwise skew analysis results.

The image alignment method described below tends to work well on images with abundant image features, or areas of significant contrast. Cars, buildings, parking lots, and roads tend to provide the best results. This approach may not work well on images which contain few features or very repetitive features, such as full canopy row crops or fields of repetitive small crops such lettuce or strawberries. We will disscuss more about the advantages and disadvantages of these methods below.

The functions behind this alignment process can work with most versions of RedEdge and Altum firmware. They will work best with versions above 3.2.0 which include the "RigRelatives" tags. These tags provide a starting point for the image transformation and can help to ensure convergence of the algorithm.

Opening Images

As we have done in previous examples, we use the micasense.capture class to open, radiometrically correct, and visualize the 5 bands of a RedEdge capture.

First, we'll load the autoreload extension. This lets us change underlying code (such as library functions) without having to reload the entire workbook and kernel. This is useful in this workbook because the cell that runs the alignment can take a long time to run, so with autoreload extension we can update the code after the alignment step for analysis and visualization without needing to re-compute the alignments each time.

In [1]:
%load_ext autoreload
%autoreload 2
In [2]:
import os, glob
import micasense.capture as capture
%matplotlib inline

panelNames = None

# # This is an older RedEdge image without RigRelatives
# imagePath = os.path.join(os.path.abspath('.'),'data','0000SET','000')
# imageNames = glob.glob(os.path.join(imagePath,'IMG_0001_*.tif'))
# panelNames = glob.glob(os.path.join(imagePath,'IMG_0000_*.tif'))

# # Image from the example RedEdge imageSet (see the ImageSet notebook) without RigRelatives.
# imagePath = os.path.expanduser(os.path.join('~','Downloads','RedEdgeImageSet','0000SET'))
# imageNames = glob.glob(os.path.join(imagePath,'000','IMG_0013_*.tif'))
# panelNames = glob.glob(os.path.join(imagePath,'000','IMG_0000_*.tif'))

# This is an altum image with RigRelatives and a thermal band
imagePath = os.path.join('.','data','ALTUM1SET','000')
imageNames = glob.glob(os.path.join(imagePath,'IMG_0245_*.tif'))
panelNames = glob.glob(os.path.join(imagePath,'IMG_0000_*.tif'))

# Allow this code to align both radiance and reflectance images; bu excluding
# a definition for panelNames above, radiance images will be used
# For panel images, efforts will be made to automatically extract the panel information
# but if the panel/firmware is before Altum 1.3.5, RedEdge 5.1.7 the panel reflectance
# will need to be set in the panel_reflectance_by_band variable.
# Note: radiance images will not be used to properly create NDVI/NDRE images below.
if panelNames is not None:
    panelCap = capture.Capture.from_filelist(panelNames)
    panelCap = None

capture = capture.Capture.from_filelist(imageNames)

if panelCap is not None:
    if panelCap.panel_albedo() is not None:
        panel_reflectance_by_band = panelCap.panel_albedo()
        panel_reflectance_by_band = [0.67, 0.69, 0.68, 0.61, 0.67] #RedEdge band_index order
    panel_irradiance = panelCap.panel_irradiance(panel_reflectance_by_band)    
    img_type = "reflectance"
    if capture.dls_present():
        img_type = "radiance"