Remote sensing image classification using soft computing approach

Mohd Faizal , Abdollah and Shahrin , Prof. Dr. , Sahib and Nanna , Prof. Dr, Suryana and Othman , Mohd (2013) Remote sensing image classification using soft computing approach. Other thesis, UTeM.

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Abstract

Mangrove forest is an important costal ecosystem in the tropical and sub-tropical coastal regions. It is among the most productivity, ecologically, environmentally and biologically diverse ecosystem in the world. With the improvement of remote sensing technology such as remote sensing images, it provides the alternative for better way of mangrove mapping because covered wider area of ground survey. Image classification is the important part of remote sensing, image analysis and pattern recognition. It is defined as the extraction of differentiated classes; land use and land cover categories from raw remote sensing digital satellite data. One pixel in the satellite image possibly covers more than one object on the ground, within-class variability, or other complex surface cover patterns that cannot be properly described by one class. A pixel in remote sensing images might represent a mixture of class covers, within-class variability, or other complex surface cover patterns. However, this pixel cannot be correctly described by one class. These may be caused by ground characteristics of the classes and the image spatial resolution This project was about the unsupervised classification for satellite image by using fuzzy logic technique. In this project, the method of unsupervised classification was implemented as compared to supervised classification. Nowadays, many situations on this earth were captured by the satellite. Therefore, it was important to be able to classify out the things or objects that had been captured by the satellite. In this project, Fuzzy Inference System (FIS) of Fuzzy Logic Toolbox in matlab was selected to do for unsupervised classification. The types of FIS technique selected to do for the classification include Fuzzy Mamdani and Fuzzy Sugeno. These two methods are used to compare which one can provide a better output. Key Researchers: Dr Mohd Faizal Abdollah Othman bin Mohd Prof Dr. Hj. Shahrin bin Sahib@Sahibuddin Prof Dr. Nanna Suryana Email: faizaiabdollah@utem.edu.my Tel. No: 06-3316662 Vote No: PJP/2009/FTMK(8D)S557

Item Type: Thesis (Other)
Uncontrolled Keywords: Image processing -- Digital techniques, Remote sensing
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
Divisions: Library > Projek Jangka Panjang / Pendek > FTMK
Depositing User: Norziyana Hanipah
Date Deposited: 28 Jul 2015 00:03
Last Modified: 28 Jul 2015 00:03
URI: http://eprints.utem.edu.my/id/eprint/14776
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