Classification of high-dimensional data

Pi-Fuei Hsieh, Purdue University

Abstract

Multispectral sensors have been used to gather data about the Earth's surface since the 1960's. Data analysis methods for multispectral data with less than 20 or so spectral bands have been studied and have given satisfactory results. As opposed to such multispectral sensors, the new generation of remote sensors, referred to as hyperspectral sensors, have hundreds of contiguous narrow spectral bands. These hyperspectral sensors, which feature high spectral resolution, fine spatial resolution, and a large dynamic range, have led to the hope that a wide variety of resources on the surface of the earth will likely be explored and identified. However, the data analysis approach that has been successfully applied to multispectral data in the past is not as effective for hyperspectral data as well. Therefore, it is necessary to investigate the problem and to explore effective approaches to hyperspectral data analysis. Our study indicates that the key problem is poor specification of the classes (inaccurate parameter estimation). We have found that the conventional approach can be retained if a preprocessing stage is established. For the preprocessing stage, we propose the lowpass spatial filter for increasing class separability and a spectral-spatial labeling method for gathering larger numbers of training samples. We also seek to combine previous work with our proposed methods to synthesize the preprocessing stage. For the main processing stage, a feature extraction method has been developed to speed up the process. As a result, an improvement in classification accuracy has been observed.

Degree

Ph.D.

Advisors

Landgrebe, Purdue University.

Subject Area

Electrical engineering|Remote sensing|Statistics

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