Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing

College

College of Computer Studies

Department/Unit

Computer Technology

Document Type

Conference Proceeding

Source Title

2014 International Conference on Advanced Computer Science and Information System (ICACSIS)

Publication Date

10-2014

Abstract

Digital images of methyl violet dye and methyl orange solutions were obtained under controlled contributions to simulate images of algal blooms. From those images, feature extraction based from both Red-Green-Blue (RGB) and Hue-Saturation-Value (HSV) color space were used. The independent variable C, which is the concentration value of the dye solution, is mapped independently with the R-channel, G-channel and B-channel as well as the H-channel, S-channel and V-channel. Linear regression and non-linear regression techniques were used to determine the best fit equation while Akaike Information Criterion (AIC) were used to compare which among the equations provide the best fit.

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Disciplines

Computer Sciences

Keywords

Remote sensing; Algal blooms; Digital images; Image processing

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