Application of principal component analysis to multisensor classification

B. R. Corner, R. M. Narayanan, S. E. Reichenbach

Research output: Contribution to journalConference article

2 Scopus citations

Abstract

The potential of high-resolution radar and optical imagery for synoptic and timely mapping in applications such as resource management, disaster delineation, and weather mapping is well-known. Numerous methods have been developed to process and quantify useful information from remotely sensed images. Most image processing techniques use texture based statistics combined with spatial filtering to separate target classes or to infer geophysical parameters from pixel radiometric intensities. The use of spatial statistics to enhance the information content of images, thereby providing better characterization of the underlying geophysical phenomena, is a relatively new technique in image processing. We are currently exploring the relationship between spatial statistical parameters of various geophysical phenomena and those of the remotely sensed image by way of principal component analysis (PCA) of radar and optical images. Issues being explored are the effects of incorporating PCA into land cover classification in an attempt to improve its accuracy. Preliminary results of using PCA in comparison with unsupervised land cover classification are presented.

Original languageEnglish (US)
Pages (from-to)202-210
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3584
Publication statusPublished - Jan 1 1999

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ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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