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.
A Michelson Fourier Transform Spectrometer senses an object/material in the time domain, producing an interferogram. To produce a spectrum, the interferogram is Fourier transformed into the spectral domain. Unless filtering is applied to the interferogram, all the time changing (AC) components of the interferogram contribute to the resulting spectrum. Aperiodic signals are not easily removed from the interferogram and, when transformed, result in false spectral features. Possible sources of real-world aperiodic signals are discussed and their effects on the resulting transformed spectra are demonstrated. Mitigation and avoidance techniques for some of the more common real- world aperiodic signals are discussed.
As the number of recognized applications for and acceptance of spectral imaging increases, the need for field spectral measurements also increases. The goal of this paper is to help ensure the quality and accuracy of field spectral measurements. Unlike laboratory measurements, where everything is controlled to meticulous detail, field measurements tend to suffer from an almost complete lack of control. Hence, assuring data quality of field measurements can be difficult. To help compensate for some of the problems that arise due to this lack of control, collection protocols are established. Even using collection protocols, sensor artifacts are not always apparent. In this paper, some of these sensor artifacts are presented and discussed. While this paper concentrates on a specific spectrometer, many of the issues, protocols and processing procedures should be generally applicable to most field spectrometers operating in this spectral region.
Adequate imagery for automated mapping of large areas became available with the successful launch of the 30-meter 7-band thematic mapper (TM) on Landsat 4 in 1982. Yet an adequate approach to automated line-of-communication (LOC) extraction continues to elude the remote sensing community. Perhaps the single biggest complicating factor is the inherently subpixel nature of the problem; almost all LOCs are narrower than current commercial sensor resolutions. Other complications include: spatial and temporal variability of LOC surface spectra, proximity to and abundance of spectrally similar materials, and atmospheric effects. We describe progress towards the detection and identification of LOCs using a technique that simultaneously extracts both spatial and spectral information. The approach currently uses a linear mixture model for simultaneously decomposing the image into fractional compositions and corresponding spectra using physical constraints. The algorithm differs from other approaches in that no traditional preprocessing or prior spatial or spectral information is required to extract the LOCs and their spectra. The algorithm has been successfully applied to TM and M-7 data. Results are presented.
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