Title

Extraction and utilization of visual features for determining plankton concentration levels from aerial images of lake water surfaces

Date of Publication

2015

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Joel P. Ilao

Defense Panel Chair

Mario O. Cordel, III
Roger Luis T. Uy
Eric Camilo R. Punzalan

Abstract/Summary

Harmful algal blooms (HABs) cause multiple problems all around the world. Algal blooms are known to cause toxicity in the water, fish kills and biomass destruction. This leads to losses in the fishing industry, tourism and the natural environment. Early detection and monitoring of HAB behavior is necessary to assess its impact on its surroundings. In recent years, remote sensing using satellite imagery and complex sensors has become popular for algae monitoring. Implementation of a smaller scale remote sensing system allows observation of less prominent bodies of water and allow flexibility in terms of the time and duration of data collection. The system is designed to mimic the functionality of satellite remote sensing systems for algae detection but using only a normal camera. The system is intended to model the correlation of concentration levels of algae with the visual features of water surfaces using regression analysis. However, visual color analysis does introduce other variables that may interfere with the visual analysis such as uneven illumination and varying lighting conditions. A methodology is proposed to determine the concentration levels by correcting for the weaknesses introduced by visual color analysis and creating a predictive model for future reference. Due to the scarcity of data, stemming from unexpected circumstances, data had to be simulated using floral foam particles as a substance with similar properties as algae particles. The system shows an average of 20% error in prediction for the simulated data. The results indicate that the information in a single and most significant color component was found to be insufficient for determining concentration levels at higher values. This can be seen as either the limitation of single variable analysis on color information for this application or that the samples reached maximum saturation since the value at which each model's accuracy drops is consistent. Color correction of the image sets produced models with increased consistency and inc

Abstract Format

html

Language

English

Format

Print

Accession Number

TU20024

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1 volume (various foliations), illustrations (some color), 28 cm.

Keywords

Algal blooms—Monitoring--Remote sensing; Remote sensing

Embargo Period

5-2-2021

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