Fun with Near-Infrared Drone Imaging

Experimenting with Drone imaging with NIR converted cameras and specialist filters has led me to develop image processing techniques to create NDVI images but it has also allowed me to create visually interesting, (borderline psychedelic!), videos and images.

Travelling with my drone to the beaches of Porto, Portugal allowed me to experience great photo opportunities of the very visually dynamic Atlantic coastline of Portugal.

Choosing different filters also makes sure that you never see the same scene twice, no matter where you are. Even a relatively overcast sky can be very vibrant and beautiful in the Near-Infrared.

Of course the vibrancy of vegetation is the easiest thing to notice in contrast with the relative darkness of other features in an image, such as Indy the cat here!

It is also interesting to notice that the fake grass on the lawn does not reflect strongly in the NIR, unlike the weeds! NIR reflectance is not merely to do with the pigment of vegetation, it is in all likelihood due to the complex structure of the spongy mesophyll layers and the water content in the xylem vessels that run through healthy vegetation, particularly leaves. This is why, as discussed previously, NIR and NDVI is a very valuable indicator of vegetation classification and of plant health.

In any event, whether the application be scientific or artistic, there is always a lot of fun to be had in opening up a new visual perspective on the everyday features of the world.

Notes:

For standard RGB batch processing of DNG (RAW) images files obtained from my DJI Mavic Pro 2 I use the following Python script which uses the rawpy wrapper for the Libraw library for convenience. Here it is:


# -*- coding: utf-8 -*-
"""
Created on Sat Oct 12 03:38:28 2019

@author: cosmi

Working Batch Processer for DNG to JPG

rawpy is an easy-to-use Python wrapper for the LibRaw library.
rawpy works natively with numpy arrays and supports a lot of options,
including direct access to the unprocessed Bayer data
It also contains some extra functionality for finding and repairing hot/dead pixels.
import rawpy.enhance for this


"""

import os
import rawpy
import imageio


for infile in os.listdir("./"):
    print( "file : " + infile)
    if infile[-3:] == "tif" or infile[-3:] == "DNG" :
       # print "is tif or DNG (RAW)"
       outfile = infile[:-3] + "jpg"
       raw = rawpy.imread(infile)
       print( "new filename : " + outfile)
       rgb = raw.postprocess()
       imageio.imsave(outfile, rgb)