Hyperspectral Spatial-Spectral Feature Classification Based on Adequate Adaptive Segmentation

This paper proposes some novel classification scheme based on adaptive spatial vicinity for hyperspectral remote sensed images. Different segmentation methods such as RobustColor Morphological Gradient (RCMG), Expectation Maximization (EM) and Recursive Hierarchical Segmentation(RHSEG) have been generalized to hyperspectral image analysis and their extensions; Hyperspectral Robust Color MorphologicalGradient (HRCMG), Adequate Expectation Maximization(AEM) and Hyperspectral Recursive Hierarchical Image Segmentation (HRHSEG) were introduced and applied in theempirical implementation. Experiments were based on two available hyperspectral data sets (Indiana Pines and Hekla).Experimental results were compared with three analysis measurements (overall accuracy, average accuracy and Kappa factor) as well as their classification maps with pixelwise methodsand some previous spatial-spectral approaches such as EMP and ECHO. All of the quantitate quality measures of proposedmethod were better than other reviewed approaches, but the classification map of proposed approach is so artificial in somecases. The novel segmentation methods (HRCMG, AEM and HRHSEG) are applied, and the accuracy was improved in compare with elder schemes, when the median voting scheme is employed.

کلیدواژه ها:

Hyperspectral images ، remote sensing ، spatialspectral classification ، Expectation Maximization ، Hierarchical Segmentation