eprintid: 10562
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/05/62
datestamp: 2023-11-09 16:37:11
lastmod: 2023-11-09 16:37:11
status_changed: 2023-11-09 16:31:41
type: article
metadata_visibility: show
creators_name: Bahari, N.I.S.
creators_name: Ahmad, A.
creators_name: Aboobaider, B.
creators_name: Bin Razali, M.F.
creators_name: Sakidin, H.
creators_name: Isa, M.S.M.
creators_name: Ananta, G.P.
creators_name: Sari, Y.A.
creators_name: Sari, N.A.
title: Remote sensing data restoration to compensate for haze effect
ispublished: pub
note: cited By 0
abstract: Remote sensing data recorded from passive satellite system tend to be degraded by attenuation of solar radiation due to haze. Haze is capable of modifying the spectral and statistical properties of remote sensing data and consequently causes problem in data analysis and interpretation. Haze needs to be removed or reduced in order to restore the quality of the data. This study aims to restore the hazy data using proposed haze removal technique and evaluate its performance by means of spectral and statistical methods. In this study, initially, haze radiances due to radiation attenuation are removed by making use of pseudo invariant features (PIFs) selected among reflective objects within the study area. Spatial filters are subsequently used to remove the remaining noise causes by haze variability. The performance of hazy data restoration was evaluated using Support Vector Machine (SVM) classification. It is revealed that the technique is able to improve the classification accuracy to the acceptable levels for data with moderate visibilities and restored the spectral and statistical properties of the data and shows an increase in overall classification accuracy from 51.63 to 82.62. © 2005 � ongoing JATIT & LLS.
date: 2018
publisher: Little Lion Scientific
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041317935&partnerID=40&md5=cc8f7bff8b124cb10890378e1f58de64
full_text_status: none
publication: Journal of Theoretical and Applied Information Technology
volume: 96
number: 1
pagerange: 118-126
refereed: TRUE
issn: 19928645
citation:   Bahari, N.I.S. and Ahmad, A. and Aboobaider, B. and Bin Razali, M.F. and Sakidin, H. and Isa, M.S.M. and Ananta, G.P. and Sari, Y.A. and Sari, N.A.  (2018) Remote sensing data restoration to compensate for haze effect.  Journal of Theoretical and Applied Information Technology, 96 (1).  pp. 118-126.  ISSN 19928645