Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops

Plants

Milton Orlando Valencia¹  Michael Gomez Selvaraj¹  Dehyro Méndez²  Claudia L. Mambuscay²  Manuel G. Forero²  María F. Monroy²  Sergio L. Miranda² 

[1] International Center for Tropical Agriculture (CIAT), Cali 763537, Colombia;Semillero Lún, Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia;

关键词: image processing;    feature detector;    feature descriptor;    Brute Force matching;    FLANN matching;    multispectral images;   

DOI  :  10.3390/plants10091791

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown