Abstract
Earth Observation (EO-1) data provides a highest spectral resolution to get spectral information of Earth's Surface targets within 242 spectral bands at 30 m spatial resolution. In this context, the main objective of this paper is to produce a land cover map using hyperspectral data acquired by EO-1 Hyperion instrument over one test site. Atmospheric correction on the hyperspectral data was performed using ENVI’s Fast Line-of-sight Atmospheric Analysis of Spectral Hyper-cubes (FLAASH) module. Support Vector Machine (SVM) classification was implemented on the dominant elements to produce a land cover map for test site. SVM is carried out in this research to deal with the multi-class issue of Hyperion data. Classification using the kernel functions in classification made the classifier robust against the outliers. The Land Cover Classification System (LCCS) was used to know the land cover classes. The result showed high accuracy for land cover map with machine learning classifier like SVM using hyperspectral remote sensing data. The overall classification accuracy obtained was 97.85.
Highlights
Land cover (LC) is very important in a lot of natural resource applications
The results showed that the Support vector machines classification (SVM) classification based on kappa coefficient 0.86 was the most accurate method.[20]
The authors evaluated various algorithms for classification in land use mapping, and concluded that the SVM algorithm in comparison with the MLC algorithms and decision trees has a higher accuracy in the preparation of land use maps
Summary
At local and regional scale, knowledge of LC forms a basic dimension of recourses available to any political unit.[1] On a wide scale, LC information is of main importance in determining the broad patterns of climate and vegetation which form the environmental framework for human activities. LC maps are a valuable contribution in the development of maintain policies for ecologically protected areas and the restoration of native environments, as well as the monitoring of desertification and land degradation in regions.[2] Remote sensing has been appropriate source for LC thematic mapping.[3] image classification[4] is the most widely used for this purpose it is the most frequently applied approach in developing land use and land cover spatial distribution maps.[5] An overview of different remote sensing classification techniques has been published.[6]
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