AVIRIS-NG hyperspectral data analysis for pre- and post-MNF transformation using per-pixel classification algorithms
Laxmi Kant Sharma, Rajani Kant Verma
Abstract
The hyperspectral image such as AVIRIS-NG provides a lot of spectral information and fine resolution that identify and discriminate similar objects based on their spectral reflectance. This study reveals a comparative analysis of multiple classification algorithms for AVIRIS-NG data, before and after minimum noise fraction (MNF) transformation for land use land cover classification. Applied distance methods are entirely based upon the availability of a complete set of endmembers for the data and used to classify the pixels in terms of endmembers. Applied classifiers yield more accurate results especially in terms of the overall accuracy after dimensionality reduction. As a result, minimum distance achieved highest 97.76% overall accuracy with a Kappa coefficient of 0.97, whereas Mahalanobis distance yields more precise results before MNF with 95.34% overall accuracy and a Kappa coefficient of 0.94. This study also indicates the importance of data dimensionality reduction for hyperspectral imagery.