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Reseach Article

Reconstruction of Temporal Images by Gradient based Sequential Prediction

by Boshir Ahmed, Md. Al Mamun, Md. Mortuza Ali
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 9
Year of Publication: 2014
Authors: Boshir Ahmed, Md. Al Mamun, Md. Mortuza Ali
10.5120/17842-8742

Boshir Ahmed, Md. Al Mamun, Md. Mortuza Ali . Reconstruction of Temporal Images by Gradient based Sequential Prediction. International Journal of Computer Applications. 102, 9 ( September 2014), 12-16. DOI=10.5120/17842-8742

@article{ 10.5120/17842-8742,
author = { Boshir Ahmed, Md. Al Mamun, Md. Mortuza Ali },
title = { Reconstruction of Temporal Images by Gradient based Sequential Prediction },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 9 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number9/17842-8742/ },
doi = { 10.5120/17842-8742 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:39.502364+05:30
%A Boshir Ahmed
%A Md. Al Mamun
%A Md. Mortuza Ali
%T Reconstruction of Temporal Images by Gradient based Sequential Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 9
%P 12-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The identification of the factors involved in change detection could lead to a comprehensive understanding of real changes and non-real changes on a broad scale, as well as prediction capability. As a huge amount of remotely sensed data is available, most of the applications require the interpretation of images collected over a period. Frequently collected satellite images mostly present strong spatial redundancies for real changes such as deforestation, urbanization, flood, bushfire etc. and non-real changes due to various system factors or environmental noise such as illumination variation and atmospheric effects. In this case, the pixel values of two images are not same. Therefore nonlinear regression prediction model such as gradient adjusted temporal prediction procedure is applied to predict a temporal image for detecting the types of changes have occurred and is presented in this paper. As the changes are detected iteratively, the whole process converges towards the final model that better defines the temporal correlation between two adjacent images.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Gradient adjusted temporal prediction temporal correlation real and non-real changes.