Joint Pattern Recognition/Data Compression Concept For Erts Multispectral Imaging

1975 ◽  
Author(s):  
Edward E. Hilbert
Keyword(s):  
Pattern Recognition ◽  
Data Compression ◽  
Multispectral Imaging ◽  
Joint Pattern

Automated Detection of Chemical Vapors by Pattern Recognition Analysis of Passive Multispectral Infrared Remote Sensing Imaging Data

2002 ◽  
Vol 56 (8) ◽  
pp. 1082-1093 ◽  
Author(s):  
Lin Zhang ◽  
Gary W. Small
Keyword(s):  
Pattern Recognition ◽  
Detection Rate ◽  
Clustering Algorithm ◽  
Multispectral Imaging ◽  
Planck Equation ◽  
Performance Criteria ◽  
Classification Rule ◽  
False Detection Rate ◽  
Imaging Data ◽  
False Detection

Pattern recognition methods are developed for the automated interpretation of passive multispectral imaging data collected from an airborne platform. Through the use of an infrared line scanner equipped with 14 spectral bandpass filters, passive infrared images are collected of an ammonia plant within a nitrogen fertilizer facility. Piecewise linear discriminant analysis is used to implement an automated algorithm for the detection of scene pixels that correspond to chemical vapor signatures. A separate classifier is used to detect the presence of hot carbon dioxide (CO2) within the images. In the assembly of training and prediction data for the development of both classifiers, the K-means clustering algorithm is used together with knowledge of the site to assign pixels to the plume/nonplume and CO2/non-CO2 categories. The effects of temperature variation within the imaged scene are removed from the data through the use of an algorithm for separating the contributions of temperature and emissivity to the Planck equation. Averaged across four data runs containing a total of 3.5 million pixels, the resulting discriminants are observed to detect approximately 91% of the plume pixels while achieving a false detection rate of less than 0.01%. The corresponding performance criteria for the CO2 classifier are a successful detection of approximately 94% of the pixels with a CO2 signature and a false detection rate of less than 0.7%. The robustness of the CO2 classifier is further enhanced through the adoption of a probability-based classification rule.

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