Development of a Watershed Algorithm for Multi-Resolution, Multi-Dimensional Clustering of Hyperspectral Data

摘要

The watershed-clustering algorithm was adapted for use in multi-dimensional spectral space and was used to define clusters in Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. This algorithm identifies clusters as peaks in a B-dimensional topographic relief, where B is the number of wavelength bands. Image pixel spectra are represented as points in this multi-dimensional space. Analysis is done at increasing values of radiometric resolution, defined by the number of segments into which each wavelength axis is divided. Segmentation of the axes divides the multi-dimensional space into bins, and the number of pixels in each bin is determined. The histogram of the bin populations defines the topography for the watershed analysis. Spectral clusters correspond to mountains or islands on this multi-dimensional surface. The algorithm is analogous to submerging this topography under water, and revealing clusters by determining when mountain peaks appear as the water surface is lowered. Testing of this algorithm reveals some surprising features. Although increasing the radiometric resolution (bins per axis) generally results in large clusters breaking up into greater numbers of small clusters, this is not always the case. Under some circumstances, the separate clusters can recombine into one large cluster when radiometric resolution is increased. This behavior is caused by the existence of single-pixel voxels, which smoothes out the topography, and by the fact that the voxels retain a surprising j degree of connectivity, even at high radiometric resolutions. These characteristics of the high-dimensional spectral data j provide the basis for further development of the watershed algorithm.