期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B7
页码：61-65
出版社：Copernicus Publications
摘要：New sensor technology has made it possible to gather multispectral images in hundreds and potentially thousands of spectral bands, this tremendous increase in spectral resolution should provide a wealth of detailed information, but the techniques used to analyze lower dimensional data often perform poorly on high dimensional data. Therefore, it is necessary to investigate the problem and to explore effective approaches to hyperspectral data analysis. Studies indicate that the key problem is to need very large number of labeled samples. It has been found that the conventional approaches can be retained if a preprocessing stage is established. Dimension reduction is a preprocessing stage that brings data from a high order dimension to a low order dimension. Some stochastic - based techniques are used for dimension reduction such as Principal Component Analysis (PCA), Discriminant Analysis Feature Extraction (DAFE) and Decision Boundary Feature Extraction (DBFE).But these techniques have some restrictions. For example PCA is computationally expensive and does not eliminate anomalies that can be seen at one arbitrary band; the number of training samples is usually not enough to prevent singularity or yield a good covariance estimate in DBFE. Spectral data reduction using Automatic Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. It is also computed in automatic fashion and can filter data anomalies. This is due to the intrinsic properties of Wavelet Transform that preserve high and low frequenc y feature therefore preserving peaks and valleys found in typical spectra. Compared to PCA, for the same level of data reduction this paper shows that automatic wavelet reduction yields better or comparable classification accuracy
