NDLI: Mangrove mapping in Penang Island by using Artificial Neural Network technique

Content Provider	IEEE Xplore Digital Library
Author	Beh Boon Chun Jafri, M.Z.M. Lim Hwee San
Copyright Year	2011
Description	Author affiliation: School of Physic, University Sains Malaysia, 11800 Minden, Penang, Malaysia (Beh Boon Chun; Jafri, M.Z.M.; Lim Hwee San)
Abstract	Environmental study is crucial in order to understand deeply about the flora and fauna living in the Earth. Mangrove forest is a unique and natural ecosystem that can be used to produce forestry product such as charcoal, timber, supply food to their surrounding marine life, and protect the inland from disturbance like erosion, flood, and tsunami. Due to the uncontrolled planning of human activity, many mangrove forests had been deforested for development of industry area, urban land and agriculture. In this study, Multi-layer Feed Forward/Multilayer Perceptrons (MLP) network system in Artificial Neural Network technique was used to map out the current state of mangrove trees. This network system require user to have a ground truth data such as in supervised classification in order to generate the training area for classification. Generally, this network comprise of a simple structure layer which consist of three layers namely input layer, hidden layer and output layer. Multi-layer Feed Forward algorithm has at least one hidden layer of neuron between the input and output layer. Each successive layer of neurons is fully interconnected with connection weight determine the strength of the connection. 2010 Thailand Earth Observing System (THEOS) satellite imagery was used as the source for the data processing with the aid of PCI Geomatica version 10.3.2 software packaging. The classification result of Multi-layer Feed Forward yield 5 category of classes. Post-classification analysis was further carried out to validate the classified data with reference data. High overall accuracy of 93.5% and kappa coefficient of 0.900 was obtained for the mangrove cover mapping. Final thematic map was produce to quantify and display the current distribution of mangrove land. The result indicates that neural network approach is suitable and reliable used for mangrove mapping.
Starting Page	245
Ending Page	249
File Size	649396
Page Count	5
File Format	PDF
ISBN	9781612849317
e-ISBN	9781612849317
DOI	10.1109/ICOS.2011.6079297
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Multi-layer Feed Forward network THEOS Mangrove Artificial neural networks Open systems Soil Multilayer perceptrons Artificial neural network Feeds Remote sensing Biological neural networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Comparison of remote sensing approach for mangrove mapping over Penang Island

Fuzzy production rule refinement using multilayer perceptrons

Efficient modeling for multilayer feed-forward neural nets

Comparison of neural network algorithms for remote sensing applications

GSM RSSI-based positioning using extended Kalman filter for training artificial neural networks

Neural Network Classification Of Multi-date Satellite Imagery

Modelling and Prediction of Internet Time-Delay by Feed-Forward Multi-layer Perceptron Neural Network

Feed-forward network training using optimal input gains

Integration of neural network and statistical image classification for land cover mapping

Mangrove mapping in Penang Island by using Artificial Neural Network technique

Similar Documents

Comparison of remote sensing approach for mangrove mapping over Penang Island

Fuzzy production rule refinement using multilayer perceptrons

Efficient modeling for multilayer feed-forward neural nets

Comparison of neural network algorithms for remote sensing applications

GSM RSSI-based positioning using extended Kalman filter for training artificial neural networks

Neural Network Classification Of Multi-date Satellite Imagery

Modelling and Prediction of Internet Time-Delay by Feed-Forward Multi-layer Perceptron Neural Network

Feed-forward network training using optimal input gains

Integration of neural network and statistical image classification for land cover mapping

Mangrove mapping in Penang Island by using Artificial Neural Network technique