Impact of spatial complexity preprocessing on hyperspectral data unmixing

For most of the success, hyperspectral image processing techniques have their origins in multidimensional signal processing with a special emphasis on optimization based on objective functions. Many of these techniques (ICA, PCA, NMF, OSP, etc.) have their basis on collections of single dimensional data and do not take in consideration any spatial based characteristics (such as the shape of objects in the scene). Recently, in an effort to improve the processing results, several approaches that characterize spatial complexity (based on the neighborhood information) were introduced. Our goal is to investigate how spatial complexity based approaches can be employed as preprocessing techniques for other previously established methods. First, we designed for each spatial complexity based technique a step that generates a hyperspectral cube scaled based on spatial information. Next we feed the new cubes to a group of processing techniques such as ICA and PCA. We compare the results between processing the original and the scaled data. We compared the results on the scaled data with the results on the full data. We built upon these initial results by employing additional spatial complexity approaches. We also introduced new hybrid approaches that would embed the spatial complexity step into the main processing stage.