Kurekin, Andriy Alexandrovich, Marshall, Andrew David ![]() |
Abstract
The impairments produced in image sensors and communication channels degrade image quality and introduce significant errors in the results of data fusion. To improve image fusion results in the presence of different types of impairments we propose a two-stage approach. New multistage nonlinear locally-adaptive image processing algorithms are developed and applied at the first stage to mitigate image impairments such as geometric distortions due to communication system synchronization errors, narrowband frequency interferences and sensor noise. Image fusion and classification algorithms based on artificial neural networks and support vector machines are used at the second stage. Experimental results are presented for real satellite remote sensing images and simulated data providing quantitative assessment of the proposed algorithms
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Date Type: | Publication |
Status: | Published |
Schools: | Computer Science & Informatics |
Subjects: | Q Science > QA Mathematics > QA76 Computer software |
Uncontrolled Keywords: | Impairment mitigation , image processing , multichannel image fusion , support vector machines |
Publisher: | IEEE |
ISBN: | 0780392868 |
Last Modified: | 24 Oct 2022 10:15 |
URI: | https://orca.cardiff.ac.uk/id/eprint/43607 |
Citation Data
Cited 3 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
![]() |
Edit Item |