A Haze Removal Technique For Satellite Remote Sensing Data Based On Spectral And Statistical Methods

• المؤلف الرئيسي: Saiful Bahari, Nurul Iman
• التنسيق: أطروحة
• اللغة: الإنجليزية; الإنجليزية
• منشور في: 2016
• الموضوعات: T Technology (General); TK Electrical engineering. Electronics Nuclear engineering
• الوصول للمادة أونلاين: http://eprints.utem.edu.my/id/eprint/18196/1/A%20Haze%20Removal%20Technique%20For%20Satellite%20Remote%20Sensing%20Data%20Based%20On%20Spectral%20And%20Statistical%20Methods%2024%20Pages.pdf; http://eprints.utem.edu.my/id/eprint/18196/2/A%20Haze%20Removal%20Technique%20For%20Satellite%20Remote%20Sensing%20Data%20Based%20On%20Spectral%20And%20Statustical%20Methods.pdf

Haze originated from forest fire burning in Indonesia has become a problem for South-east Asian countries including Malaysia. Haze affects data recorded using satellite due to attenuation of solar radiation by haze constituents. This causes problems to remote sensing data users that require continuous data, particularly for land cover mapping. There are numbers of haze removal techniques but these techniques suffer from limitations since they are developed and designed best for particular regions, i.e. mid-latitude and high-latitude countries. Almost no haze removal techniques are developed and designed for countries within equatorial region where Malaysia is located. This study is meant to identify the effects of haze on remote sensing data, develop haze removal technique that is suitable for equatorial region, especially Malaysia and evaluate and test it. Initially, spectral and statistical analyses of simulated haze datasets are carried out to identify the effects of haze on remote sensing data. Land cover classification using support vector machine (SVM) is carried out in order to investigate the haze effects on different land covers. The outcomes of the analyses are used in designing and developing the haze removal technique. 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 technique is applied on simulated hazy dataset for performance evaluation and then tested on real hazy dataset. It is revealed that, the technique is able to remove haze and improve the data usage for visibility ranging from 6 to 12 km. Haze removal is not necessary for data with visibility more than 12 km because able to produce classification accuracy more than 85%, i.e. the acceptable accuracy. Nevertheless, for data with visibility less than 6 km, the technique is unable to improve the accuracy to the acceptable one due to the severe modification of spectral and statistical properties caused by haze.