Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12313/2666
Title: Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops
Authors: Forero, Manuel G.
Mambuscay, Claudia L.
Monroy, María F.
Miranda, Sergio L.
Méndez, Dehyro
Valencia, Milton Orlando
Gomez Selvaraj, Michael
Keywords: Image processing
Feature detector
Feature descriptor
Brute Force matching
FLANN matching
Multispectral images
Issue Date: 28-Aug-2021
Publisher: Plants
Citation: Forero, M.G.; Mambuscay, C.L.; Monroy, M.F.; Miranda, S.L.; Méndez, D.; Valencia, M.O.; Gomez Selvaraj, M. Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops. Plants 2021, 10, 1791. https://doi.org/10.3390/plants10091791
Abstract: Precision agriculture has greatly benefited from advances in machine vision and image processing techniques. The use of feature descriptors and detectors allows to find distinctive keypoints in an image and the use of this approach for agronomical applications has become a widespread field of study. By combining near infrared (NIR) images, acquired with a modified Nikon D80 camera, and visible spectrum (VIS) images, acquired with a Nikon D300s, a proper crop identification could be obtained. Still, the use of different sensors brings an image matching challenge due to the difference between cameras and the possible distortions from each imaging technique. The aim of this paper is to compare the performance of several feature descriptors and detectors by comparing near infrared and visual spectral bands in rice crop images. Therefore, a group of 20 different scenes with different cameras and growth stages in a rice crop were evaluated. Thus, red, green, blue (RGB) and L, a, b (CIE L*a*b*) channels were extracted from VIS images in order to compare the matches obtained between each of them and the corresponding NIR image. The BRISK, SURF, SIFT, ORB, KAZE, and AKAZE methods were implemented, which act as descriptors and detectors. Additionally, a combination was made between the FAST algorithm for the detection of keypoints with the BRIEF, BRISK, and FREAK methods for features description. BF and FLANN matching methods were used. The algorithms were implemented in Python using OpenCV library. The green channel presented the highest number of correct matches in all methods. In turn, the method that presented the highest performance both in time and in the number of correct matches was the combination of the FAST feature detector and the BRISK descriptor.
URI: https://www.mdpi.com/2223-7747/10/9/1791
ISSN: 2223-7747
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