Comparative Analysis of Feature and Intensity Based Image Registration Algorithms in Variable Agricultural Scenarios
Abstract
Image registration has widespread application in fields like medical imaging, satellite imagery and agriculture precision as it is essential for feature detection and extraction. The extent of this paper is focussed on analysis of intensity and feature-based registration algorithms over Blue and RedEdge multispectral images of wheat and cauliflower field under different altitudinal conditions i.e., drone imaging at 3 m for cauliflower and handheld imaging at 1 m for wheat crops.
The overall comparison among feature and intensity-based algorithms is based on registration quality and time taken for feature matching. Intra-class comparison of feature-based registration is parameterized on type of transformation, number of features being detected, number of features matched, quality and feature matching time.
Intra-class comparison of intensity-based registration algorithms is based on type of transformation, nature of alignment, quality and feature matching time. This study has considered SURF, MSER, KAZE, ORB for feature-based registration and Phase Correlation, Monomodal intensity and Multimodal intensity for intensity-based registration.
Quantitatively, feature-based techniques were found superior to intensity-based techniques in terms of quality and computational time, where ORB and MSER scored highest. Among intensity-based methods, Monomodal intensity performed best in terms of registration quality.
However, Phase Correlation marginally scored less in quality but fared well in terms of computational time.