Image Analysis On Remote Sensing Images
Humans easily identify roads in remote sensing images, but this task is difficult to automate using computers. In order to identify road segments from satellite images, human beings (experts) seem to first search for a set of linear and curvilinear features and then apply knowledge or use experi...
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| Format: | Monograph |
| Language: | English |
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Universiti Sains Malaysia
2006
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| Online Access: | http://eprints.usm.my/58709/ http://eprints.usm.my/58709/1/Image%20Analysis%20On%20Remote%20Sensing%20Images_Azratul%20Husna%20Azuddin.pdf |
| Summary: | Humans easily identify roads in remote sensing images, but this task is difficult to
automate using computers. In order to identify road segments from satellite images, human
beings (experts) seem to first search for a set of linear and curvilinear features and then
apply knowledge or use experience to decide (or guess) whether these linear and
curvilinear features are roads are not. From the image, the roads can be detected in a single
line to perform as a map or digital map nowadays. The selected image that I used here is a
satellite image of an area which had a network of roads. The satellite image is analyzed
from one step to another step in different process using MATLAB software. The important
processes must be done to the satellite image before the single road lines can be detected.
A detection theory is used which can be done by computer to overcome drawbacks of
current theories and detect major roads in an image with high speed and high precision.
The technique is combining image segmentation and image enhancement process. In this
project, the enhancement of the satellite image is the important process in road detection
process. Firstly, an image in any file formats is load/save into the workspace folder in
MATLAB. The input image is converted into grayscale form. Before continue the next
step, first the grayscale image is enhanced for better intensity. In this process, many
function of enhancement are used to get the better image for the output. The function that
used for the enhancement processes are imadjust, histeq, and adapthisteq. Comparing all
the result using those functions, the best-enhanced image is selected to the next step. The
process of image segmentation that involve in this project is edge detection. The edge
detection is done using Laplacian of Gaussian detector. The segmentation process required
an intensity image for all the process involved. |
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