Paper
21 December 1999 Pattern recognition and image processing for environmental monitoring
Author Affiliations +
Abstract
Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Khalid J. Siddiqui and DeLyle Eastwood "Pattern recognition and image processing for environmental monitoring", Proc. SPIE 3853, Environmental Monitoring and Remediation Technologies II, (21 December 1999); https://doi.org/10.1117/12.372876
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Feature extraction

Image classification

Pattern recognition

Feature selection

Statistical analysis

Chemical analysis

Classification systems

RELATED CONTENT


Back to Top