Development of super resolution techniques for finer scale remote sensing image mapping

Author:

Li, Feng [claim]

Description:

In this thesis, methods for achieving finer scale multi-spectral classification through the use of super resolution (SR) techniques are investigated. A new super resolution algorithm Maximum a Posteriori based on the universal hidden Markov tree model (MAP-uHMT) is developed which can be applied successfully to super-resolve each multi-spectral channel before classification by standard methods. It is believed that this is the first time that a true super resolution algorithm has been applied to multi-spectral classification, and results are shown to be excellent. Image registration is an important step for SR in which misalignment can be measured for each of many low resolution images; therefore, a new and computationally efficient image registration is developed for this particular application. This improved elastic image registration method can deal with a global affine warping and local shift translations based on coarse to fine pyramid levels. The experimental results show that it can provide good registration accuracy in less computational time than comparable methods. Maximum a posteriori (MAP) is adopted to deal with the ill-conditioned problem of super resolution, wherein a prior is constructed based on the universal hidden Markov tree (uHMT) model in the wavelet domain. In order to test this prior for MAP estimation, it is first tested in the simpler and typically ill-conditioned problem of image denoising. Experimental results illustrate that this new image denoising method achieves good performance for the test images. The new prior is then extended to SR. By combining with the new elastic image registration algorithm, MAP-uHMT can super resolve both some natural video frames and remote sensing images. Test results with both synthetic data and real data show that this method achieves super resolution both visually and quantitatively. In order to show that MAPuHMT is also applicable more widely, it is tested on a sequence of long-range surveillance images captured under conditions of atmospheric ...

Publisher:

UNSW, Sydney

Year of Publication:

2009

Document Type:

doctoral thesis ; http://purl.org/coar/resource_type/c_db06 ; [Doctoral and postdoctoral thesis]

Language:

Subjects:

Resolution (Optics) ; Image processing : mathematical models ; Hidden Markov models ; Remote sensing : mathematical models ; Digital mapping ; Algorithms

DDC:

004 Data processing & computer science (computed)

Rights:

open access ; https://purl.org/coar/access_right/c_abf2 ; CC BY-NC-ND 3.0 ; https://creativecommons.org/licenses/by-nc-nd/3.0/au/ ; free_to_read

Terms of Re-use:

CC-BY-NC-ND

Relations:

http://hdl.handle.net/1959.4/44098 ; https://doi.org/10.26190/unsworks/22438

URL:

http://hdl.handle.net/1959.4/44098
https://unsworks.unsw.edu.au/bitstreams/bffc6f73-88f5-48d2-aa4f-488680750724/download
https://doi.org/10.26190/unsworks/22438

Content Provider:

UNSW Sydney (The University of New South Wales): UNSWorks