Texture analysis using gray level Co-Occurence matrix (GLCM) for water quality index at Sungai Muda, Kedah / Nur Syahira Ruslee

The surface quality of an object is called the texture and the surface of any visible object is textured at certain scale. As well, the variation of light or dark patterns of various textures are indications for visual enjoying. Moreover, texture is a feature used as a border for images into the cer...

Full description

Saved in:
Bibliographic Details
Main Author: Ruslee, Nur Syahira
Format: Thesis
Language:English
Published: 2020
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/28303/1/TD_NUR%20SYAHIRA%20RUSLEE%20AP%20R%2019_5.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The surface quality of an object is called the texture and the surface of any visible object is textured at certain scale. As well, the variation of light or dark patterns of various textures are indications for visual enjoying. Moreover, texture is a feature used as a border for images into the certain regions or places and it can be used to classify those regions. The texture provides the information in spatial arrangement of colours or intensities in an image. The aim of this project is to produce Gray Level Co-Occurrence Matrix (GLCM) mapping at Sungai Muda. The objective of this study is to generate the four texture parameters (contrast, entropy, correlation, and homogeneity) using the GLCM at Sungai Muda, Kedah and then produce the four maps of the classification based on the grey value own each parameter. Next, to identify the most significant parameter using regression analysis and mapping the distribution of WQI based on the most significant parameter. The in-situ data (water quality parameters) were obtained from Department of Environment Malaysia (DOE) and satellite image of Geo-Eye 1 with spatial resolution 0.5m was obtained from Agency Remote Sensing Malaysia (ARSM). The three software were used in this project such as ERDAS imagine, ENVI and ArcGIS. The results show four maps from the GLCM method and the most significant GLCM map will produces the distribution of the WQI map.