8 enhancement of its visual qualities in order to make it more interpretable by a
human analyst Richards and Jia 2006.
2.2.2 Image Pre-processing
Image pre-processing is concerned to perform correction on any errors that occurred in the remotely sensed images. There are two common types of image
pre-processing techniques, namely radiometric correction and geometric correction. Radiometric correction, aim to remove errors on remotely-sensed data,
either resulted from the presence of the atmosphere as a transmission medium through which radiation must travel from its source to the sensors, or from
instrumentation effects Richards and Jia 2006. Atmospheric correction might be a necessary pre-processing technique to compute a ratio of the values in two bands
of a multispectral, relate upwelling radiance from a surface to some property of that surface in terms of a physically based model, and compare results or ground
measurements made at one time to results achieved at a later Mather 2004. Radiometric errors within a band and between bands may due to effects of design
and operation of the sensor system which normally ignored by comparison to band errors from atmospheric effects. An ideal radiation detector should have a
transfer characteristic radiation in, signal out as shown in Figure 1.a. which should be linear, and therefore there is a proportional increase and decrease of
signal with detected radiation level Richards and Jia 2006.
a b
Figure 1. Transfer characteristic of a radiation detector: a. Ideal transfer
characteristic, b. Hypothetical mismatches in detector characteristics in the same
band Richards and Jia 2006.
9 The transformation of a remotely sensed image is called geometric
correction or geo-referencing. A related technique, called registration, is the fitting of the coordinate system of one image to that of the second image of the same
area. Accurate image registration is needed if a time sequence of images is used to detect changes in, for example, the land covers of an area date Mather 2004.
2.2.3 Image Processing
Image data, available in digital form, can be quantized spatially and radiometrically. There are several approaches are possible in extracting the
information. Two approaches are usually used to extract information from digital image data: quantitative analysis or also called classification and photo-
interpretation or sometimes called visual image interpretation. Photo- interpretation is aided substantially if a degree of digital image processing is
applied to the image data beforehand, while quantitative analysis depends for its success on information provided at key stages by an analyst Richards and Jia
2006. Photo-interpretation which involves direct human interaction and therefore
it needs high level decisions. It is good for spatial assessment but poor in quantitative accuracy. Area estimated by photo-interpretation, for instance, would
involve planimetric measurement of regions identified visually; in which, boundary definition errors will prejudice area accuracy. By contrast, quantitative
analysis, requiring little human interaction, has poor spatial ability but high quantitative accuracy. Its high accuracy comes from the ability of a computer, if
required, to process every pixel in a given image and to take account of the full range of spectral, spatial and radiometric detail present. Its poor spatial properties
come from the relative difficulty with which decisions about shape, size, orientation and texture can be solved by using standard sequential computing
techniques Richards and Jia 2006. In computer-based quantitative analysis, the attributes of each pixel such
as the spectral bands available are examined in order to give the pixel a label which identify it as belong to a particular class of pixels of interest to the user
Richards and Jia 2006. The process of classification consists of two stages:
