Name
Abstract | ||
|---|---|---|
Through a series of specific examples, some characteristics encountered in analyzing high-dimensional multispectral data are illustrated. The increased importance of the second-order statistics in analyzing high-dimensional data is shown, as is the shortcoming of classifiers such as the minimum distance classifier, which rely on first-order variations alone. It is also shown how inaccurate estimation of first- and second-order statistics, e.g., from use of training sets which are too small, affects the performance of a classifier. Recognizing the importance of second-order statistics on the one hand, but the increased difficulty in perceiving and comprehending information present in statistics derived from high-dimensional data on the other, the authors propose a method to aid visualization of high-dimensional statistics using a color coding scheme |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1109/36.239901 | IEEE T. Geoscience and Remote Sensing |
Keywords | Field | DocType |
geophysical techniques,method,remote sensing,geophysics computing,image coding,second-order statistics,pattern recognition,data analysis,training sets,image recognition,visualization,measurement,high-dimensional multispectral data,geophysics,color coding scheme,classifier,technique,classification,data processing,testing,random variables,estimating,data visualization,statistical analysis,first order,image sensors,spectroscopy,high dimensional data,multispectral imaging,color coding,earth,statistics | Data mining,Data processing,Computer science,Remote sensing,Image processing,Multispectral pattern recognition,Artificial intelligence,Classifier (linguistics),Computer vision,Color-coding,Data visualization,Visualization,Multispectral image | Journal |
Volume | Issue | ISSN |
31 | 4 | 0196-2892 |
Citations | PageRank | References |
70 | 15.94 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chulhee Lee | 1 | 454 | 86.37 |
| David A. Landgrebe | 2 | 807 | 125.38 |