A technique has been developed to quantitatively assess the relative radiometric accuracy of each detector at the
focal plane of a pushbroom hyperspectral sensor. The approach requires only a calibrated radiance data cube as
input. The results are visualized as a 2-D error image, analyzed statistically, and presented graphically as 2-D
probability distribution function plots. We present results comparing the performance of the MaRS, COMPASS,
HYCAS, and SpecTIR sensors. We also describe the algorithm basis, assumptions, and implementation details.
In many applications of remotely-sensed imagery, one of the first steps is partitioning the image into a tractable number of regions. In spectral remote sensing, the goal is often to find regions that are spectrally similar within the region but spectrally distinct from other regions. There is often no requirement that these region be spatially connected. Two goals of this study are to partition a hyperspectral image into groups of spectrally distinct materials, and to partition without human intervention. To this end, this study investigates the use of multi- resolution, multi-dimensional variants of the watershed- clustering algorithm on Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. The watershed algorithm looks for clusters in a histogram: a B-dimensional surface where B is the number of bands used (up to 210 for HYDICE). The algorithm is applied to HYDICE data of the Purdue Agronomy Farm, for which ground truth is available. Watershed results are compared to those obtained by using the commonly-available Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm.
The watershed-clustering algorithm was adapted for use in multi-dimentional 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 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 smooths out the topography, and by the fact that the voxels retain a surprising degree of connectivity, even at high radiometric resolutions. These characteristics of the high-dimensional spectral data provide the basis for further development of the watershed algorithm.
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