Developing a model to increase quality of DEM

Download

index.pdf

Date

2013

Author

Paşaoğulları, Onur

Metadata

Show full item record

Item Usage Stats

134
views

84
downloads

Low resolution (LR) Grid Digital Elevation Models (DEMs) are the inputs of multi frame super resolution (MFSR) algorithm to obtain high resolution (HR) grid DEM. In digital image MFSR, non-redundant information carrying LR image pairs are a necessity. By using the analogy between digital image and grid DEMs, it is proven that, although the LR grid DEMs have a single source, they carry non-redundant information and they can be inputs of MFSR. Quality of grid DEM can be increased by using MFSR techniques. The level of spatial enhancement is directly related to the amount of non-redundant information that the LR grid DEM pairs carry. It is seen that super resolution techniques have potential to increase the accuracy of grid DEMs from a limited sampling.

Subject Keywords

Digital elevation models., Spatial data infrastructures., Geospatial data., Digital elevation models.

URI

http://etd.lib.metu.edu.tr/upload/12615756/index.pdf
https://hdl.handle.net/11511/22562

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

PROGRESSIVE COMPRESSION OF DIGITAL ELEVATION DATA USING MESHES Kose, Kivanc; Yılmaz, Erdal; ÇETİN, AHMET ENİS (2009-07-17) In this paper a new Digital Elevation Map (DEM) image compression algorithm is proposed. DEM image can be threated as a grayscale image, whose pixel values are the elevation values of the map points. The grayscale DEM image is compressed using an adaptive wavelet based image compression algorithm. The method, which is an extension of the progressive mesh compression takes advantage of the multiresolution property of the wavelets while coding the map images. This makes it possible to decode different resolut...
An Investigation on hyperspectral image classifiers for remote sensing Özdemir, Okan Bilge; Çetin, Yasemin; Department of Information Systems (2013) Hyperspectral image processing is improved by the capabilities of multispectral image processing with high spectral resolution. In this thesis, we explored hyperspectral classification with Support Vector Machines (SVM), Maximum Likelihood (ML) and KNearest Neighborhood algorithms. We analyzed the effect of training data on classification accuracy. For this purpose, we implemented three different training data selection methods; first N sample selection, randomly N sample selection and uniformly N sample se...
An embedding technique to determine tau tau backgrounds in proton-proton collision data Sirunyan, A. M.; et. al. (IOP Publishing, 2019-06-01) An embedding technique is presented to estimate standard model ττ backgrounds from data with minimal simulation input. In the data, the muons are removed from reconstructed μμ events and replaced with simulated tau leptons with the same kinematic properties. In this way, a set of hybrid events is obtained that does not rely on simulation except for the decay of the tau leptons. The challenges in describing the underlying event or the production of associated jets in the simulation are avoided. The technique...
An iterative adaptive multi-modal stereo-vision method using mutual information Yaman, Mustafa; Kalkan, Sinan (2015-01-01) We propose a method for computing disparity maps from a multi-modal stereo-vision system composed of an infrared-visible camera pair. The method uses mutual information (MI) as the basic similarity measure where a segment-based adaptive windowing mechanism is proposed along with a novel MI computation surface with joint prior probabilities incorporated. The computed cost confidences are aggregated using a novel adaptive cost aggregation method, and the resultant minimum cost disparities in segments are plan...
An algorithm for line matching in an image by mapping into an n-dimensional vector space Sultanov, Raiymbek; Atakan, Ahmet; Ismailova, Rita (2019-01-01) This paper proposes a minimal length difference algorithm for construction of a line in an image by solving the problem of optimal contour approximation. In this algorithm, a method for finding interest points is proposed, and the object matching (classification) is done by mapping interest points onto a vector space. In cases where the lines in the representation of the images are not smooth, the algorithm converges rapidly. The results of the experiments showed that for convergence of the contour simplifi...

Citation Formats

O. Paşaoğulları, “Developing a model to increase quality of DEM,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.