Hybrid Invariant Local Features for Multiple Satellite Image Matching and Registration
N.S. Anil1, Chandrappa D.N2
1N.S. Anil*, Department of Electronics and Communication Engineering, East West Institute of Technology, Bangalore, India.
2Chandrappa D.N, Department of Electronics and Communication, Engineering, SJB Institute of Technology, BGS Health and Education City, Kengeri, Bangalore, India.
Manuscript received on November 14, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 309-314 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6274129219/2019©BEIESP | DOI: 10.35940/ijitee.B6274.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Automatic Image Registration, BRISK, Feature from Accelerated Segment Test Feature, Inliers Ratio, Repeatability.
Scope of the Article: Image Processing and Pattern Recognition