A Hyperspectral Remote Sensing Image Endmember Extraction Algorithm Based on Modified Extendedmorphological Operator

Applying the morphological operator,which characterizes the spatial correlative informations of pixels,to endmember extraction of hyperspectral remote sensing image can improve the performance of algorithm effectively.In order to overcome the limitations in sorting rules and replacing criteria of extended-morphological operator,which is commonly used in hyperspectral remote sensing image to extract endmembers,the modified extended-morphological operator is proposed after introducing the concept and presenting the calculating method of reference vector.The cross-replacement phenomena at the junction of different classes can be avoided and the correct coverage direction can be ensured when the modified sorting rules and replacing criteria have been applied in endmember extraction algorithm to enhance the results as key means.The endmember extraction algorithm using the determine profiles,generated after open-close and close-open operations defined by basic dilation and erosion operations of modified extended morphology,is described in detail.The automated modified extended-morphological endmember extraction algorithm is achieved by using both spatial and spectral information in a combined manner,thus,the endmember extraction result is superior to the approachs designed from a spectral information viewpoint only.The alogrithm is implemented in IDL7.0 and testd by using real hyperspectral imagery collected by airborne visible/infrared imaging spectrometer in cuprite area,the experimental results of the similarity on spectral curves,the average similarity and the mineral distribution maps verified the validity of the algorithm.