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IV. RESULT AND DISCUSSION
4.1 Land Use Classification
Land use classification is done in order to derive the information of land use categories from remotely sensed data in the research site which then can be
used as basis data for various research analyses. In this research, land use classification has been done through two main processes that are image pre-
processing and image processing. Land use classification has been conducted to LANDSAT images 2002, 2005 and 2008 of Siak District. This time series of land
use history are needed for detecting the land use transitions that happen in the research site during 2002 – 2005 and 2005 - 2008.
4.1.1 Image Pre-processing
LANDSAT image may consist of some errors which might be caused by the atmospheric condition andor sensor malfunction that can affect the quality of
the image will be interpreted. In this research, image pre-processing has been done in order to prepare LANDSAT images for subsequent analysis that attempts
to correct or compensate for systematic errors. There were two processes in term of image pre-processing which have been done in this research that are SLC-OFF
Gap Filling and Geometric Correction. Nowadays, LANDSAT 7 ETM+ images are delivered to the user contains
stripping gaps which usually also called SLC-OFF Gaps. These gaps could not be interpreted because the gaps have no value no data. Common methods to fix
these gaps are by 1 interpolating the values from neighbor pixels or 2 filling the gaps of the SLC-OFF image primary scene image with other LANDSAT
image fill scene image. As long as the fill scene image has characteristic similarity with the primary image, the second method is more suitable because the
gaps of primary image will be replaced by the modified pixels from the fill scene image. The SLC-OFF Gap Filling process in this research has been done by using
Frame and Fill tool
which developed
by Richard
Irish in affiliation with NASA Goddard Space Flight Center. In theory, the pixel values
of the primary scene can be generated by applying a corrective gain and bias to
37 the pixel values of the fill scene which can be found using the mean and standard
deviation of the data. For greater precision and a product which is visibly better looking, the corrective gains and biases can be calculated in a moving window
around each pixel in the scene. This is the basis of the localized linear histogram match LLHM Scaramuzza, et al. 2004. In this research, only LANDSAT
images 2005 and 2008 have been processed by SLC-OFF Gap Filling in order to fix the SLC-OFF gaps which appear on the images.
Figure 11. SLC-OFF Gap Filling. A SLC-OFF Image, B Overlaid Image: SLC-OFF image and the fill scene image, and C SLC-OFF Gap Filled Image
Geometric correction aims to correct positional and geometrical errors that may be occurred on the remotely sensed data. Geometric correction uses one
reference data that is known has fine positional and geometrical properties regarding to the real world coordinate system. Base Maps of Riau Province acted
as reference data in the geometric correction process which means the LANDSAT images and other related spatial data should refer to the Base Maps of Riau
Province as spatial references. The geometric correction process used Polynomial Geometric Model which uses polynomial coefficients to map between image
spaces. The order of the polynomial may be from one up to five with no enforced
A B
C