Affiliations: [a] The College of Staten Island/CUNY, Department Engineering Science and Physics, The City University of New York, 2800 Victory Boulevard, Staten Island, NY 10314-6600, USA. Tel.: +1 718 982 2972; Fax: +1 718 982 2830; E-mail: [email protected] | [b] Lockheed Martin NE&SS Surface Systems, Moorestown, NJ, USA. Tel.: +1 856 722 4720; Fax: +1 856 722 2207; E-mail: [email protected] | [c] Electrical Engineering and Computer Science Department, Tufts University, Medford, MA 02155, USA. Tel.: +1 617 627 3217; Fax: +1 617 627 3220; E-mail: [email protected]
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Abstract: The significant advance in high spatial and spectral resolution sensors technology on board remote sensing spacecraft has increased, by order of magnitude, the spectral bands or features dimensionality of datasets. The need arises, in remote sensing, for feature dimensionality reduction to offset the error in estimation of the class statistics. Principal component analysis (PCA) or KLT the image dependent transform is a useful technique in reducing the dimensionality of the datasets at a high costs of hardware and O(N3) computational operations. This paper discusses the development of partially signal dependent classes of sequential slant Hadamard transforms that require elementary operations and can be used for multispectral classifications.
