Improvement of SAM Land Cover Classification of airborne hyperspectral dataUsing expert system

Abstract

Hyperspectral imaging has many applications such as Land Use / Land Cover mapping, urban planning, mineral exploration, environmental monitoring, and military surveillance. This research is concerned with the investigation of the improvement of urban land cover mapping using airborne hyperspectral data. Geometric correction and atmospheric correction have been conducted. Quality of rectification has been assessed using DGPS check points. It was found that the total RMS of airborne hyperspectral data was 2.7372m. Minimum Noise Fraction (MNF) transformation and Pixel Purity Index (PPI) have been used for spectral and spatial data reduction. After that feature selection has been carried out. In this research a supervised classification method has been applied using the Spectral Angle Mapper,then, the discrimination of urban feature has been improved using expert system. An expert system classification was developed using the knowledge engineer of ERDAS Imagine 2010 for post classification refinement of initially classified output. The primary motivation behind using the expert system was to reclassify the initial Spectral Angle Mapper classification and reduce errors of omission and commission. Results revealed that the overall accuracy of SAM was 90% and kappa index was 0.89 whereas the expert system gives slightly higher overall accuracy 94.5 percent and a kappa index was 0.93.