Improved iterative error analysis using spectral similarity measures for vegetation classification in hyperspectral images

Iterative error analysis (IEA) is one of popular, sequential and linear constrained endmember extraction algorithm that uses spectral angle mapping (SAM) to calculate angles between spectral vectors. However, IEA has a limit that discriminating similar spectral vector is difficult because SAM does not consider positive and negative correlations. Since vegetation has similar spectral properties, it is difficult to classify different vegetation types. To improve IEA for various applications, such as crop classification and change detection, spectral similarity measures other than SAM have been applied to IEA. Many spectral similarity measures have been developed to calculate the similarities among spectral signatures and these are divided into the original methods and the newly developed hybrid algorithms. In this study, the original methods used were SAM, SCA, and SID, while the hybrid methods included SAMSID, SCASID, Jeffries-matusita measures-SAM (JMSAM), and normalized spectral similarity score (NS3). A Compact airborne spectrographic imager image including three crops and road was used and similarity values of four endmembers extracted by modified IEA were calculated. The CASI image was classified using endmembers and minimum distance classifier. The classification accuracy of the modified IEA with SMA, SCA, SID, SAMSID, SCASID, JMSAM, and NS3 were 84.45%, 85.56%, 61.47%, 65.83%, 62.11%, 93.47%, 90.29%. SID based algorithm has lower accuracy because SID tends to make two similar spectral signatures more similar. The results showed that JASAM was most effective to classify different vegetation types. The modified IEA with JMSAM could classify vegetation more effectively than the original IEA.