A method for improved localization of edges in multi/hyperspectral imagery

We propose an efficient approach for achieving improved localization of edges detected in remotely sensed imagery wherein the improvement is in the localization of the detected edges. This work is based on the notion that the partial derivatives of individual image components used for vector gradient computation often yields thick edges, and consequently optimizing them to only constitute contributions towards their local scalar gradient maxima before being employed in a vector field gradient calculation can yield significantly localized edges in the final edge map. Our approach was tested on several remotely sensed multispectral and hyperspectral datasets with favorable results.