Open AccessHybrid Invariant Local Features for Multiple Satellite Image Matching and Registration
Author(s) -
Nayana Anil,
Chandrappa D.N
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6274.129219
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , matching (statistics) , computer vision , invariant (physics) , local binary patterns , feature (linguistics) , feature extraction , feature matching , binary number , hyperspectral imaging , scale invariant feature transform , image registration , image (mathematics) , histogram , mathematics , linguistics , statistics , philosophy , arithmetic , mathematical physics
Automatic image registration (IR) is very challenging and very important in the field of hyperspectral remote sensing data. Efficient autonomous IR method is needed with high precision, fast, and robust. A key operation of IR is to align the multiple images in single co-ordinate system for extracting and identifying variation between images considered. In this paper, presented a feature descriptor by combining features from both Feature from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Key point (BRISK). The proposed hybrid invariant local features (HILF) descriptor extract useful and similar feature sets from reference and source images. The feature matching method allows finding precise relationship or matching among two feature sets. An experimental analysis described the outcome BRISK, FASK and proposed HILF in terms of inliers ratio and repeatability evaluation metrics.