Classification of biological and non-biological fluvial particles using image processing and artificial neural network

Bim Prasad Shrestha; Nabin Kumar Shrestha; Laxman Poudel

doi:10.1117/12.818655

19 March 2009 Classification of biological and non-biological fluvial particles using image processing and artificial neural network

Bim Prasad Shrestha, Nabin Kumar Shrestha, Laxman Poudel

Author Affiliations +

Proceedings Volume 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII; 73430K (2009) https://doi.org/10.1117/12.818655
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Particles flowing along with water largely affect safe drinking water, irrigation, aquatic life preservation and hydropower generation. This research describes activities that lead to development of fluvial particle characterization that includes detection of biological and non-biological particles and shape characterization using Image Processing and Artificial Neural Network (ANN). Fluvial particles are characterized based on multi spectral images processing using ANN. Images of wavelength of 630nm and 670nm are taken as most distinctive characterizing properties of biological and non-biological particles found in Bagmati River of Nepal. The samples were collected at pre-monsoon, monsoon and post-monsoon seasons. Random samples were selected and multi spectral images are processed using MATLAB 6.5. Thirty matrices were built from each sample. The obtained data of 42 rows and 60columns were taken as input training with an output matrix of 42 rows and 2 columns. Neural Network of Perceptron model was created using a transfer function. The system was first validated and later on tested at 18 different strategic locations of Bagmati River of Kathmandu Valley, Nepal. This network classified biological and non biological particles. Development of new non-destructive technique to characterize biological and non-biological particles from fluvial sample in a real time has a significance breakthrough. This applied research method and outcome is an attractive model for real time monitoring of particles and has many applications that can throw a significant outlet to many researches and for effective utilization of water resources. It opened a new horizon of opportunities for basic and applied research at Kathmandu University in Nepal.

Citation Download Citation

Bim Prasad Shrestha, Nabin Kumar Shrestha, and Laxman Poudel "Classification of biological and non-biological fluvial particles using image processing and artificial neural network", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73430K (19 March 2009); https://doi.org/10.1117/12.818655

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Particles

Image processing

Neural networks

Water

Reflectivity

Artificial neural networks

Image classification

Show All Keywords

Keywords/Phrases

Search In:

Publication Years