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Introduction
Remotely sensed images are major sources of data and information that are used in various fields such as environmental studies, forest management, and urban change detection. One of the products of the images is a thematic map. So far many efforts have been performed to extract information from remotely sensed images and various methods have been developed in this field. One of the main approaches is quantitative analysis (digital interpretation). Among digital techniques, classification is a common and powerful information extraction method, which is used in remote sensing. There are many classification methods that have their own advantages and drawbacks. Between classification methods, maximum likelihood approach has been used more frequently. Standard classification methods usually concern pixels as main elements and try to label the pixels individually. But, their results are not perfect and always are erroneous, since many steps are introducing errors in the classification process. Initial data (pixels) have influenced by some errors. The error sources will be explained to understand better benefits of this paper.

Future work may well produce an integrating method from which a user can select a mix appropriate to the spatial, spectral, and temporal resolution of the data in hand and information output desired (Richards, 1993). The purpose of this paper is to show how some knowledge such as prior information about the expected distribution of classes in a final classification map can be used to improve classification accuracies. Prior information is incorporated through the use of prior probabilities-that is, probabilities of occurrence of classes which are based on separate, independent knowledge concerning the area to be classified. Used in their simplest form, the probabilities weight the classes according to their expected distribution in the output dataset by shifting decision space boundaries to produce larger volumes in measurement space for classes that are expected to be large and smaller volumes for classes expected to be small.

Error Sources
Initial data (pixels) have influenced by some errors. The error sources will be:

a) During data acquisition process:
Data acquisition process in remote sensing affects the reflectance measured by the sensor and then some errors are introduced in the entered data, ands subsequently into the classification procedure. Atmosphere as a medium for transforming the energy from sun to the objects and from objects to the sensors, can affect the recorded brightness values. Atmospheric layers change the pixel brightness values in two ways, absorption and scattering. These two effects, change real brightness values, and therefore disturb the classification accuracy. But this error can be neglected relative to the others. Indeed if the application is not a specific case then the atmosphere errors can be ignored and the corresponding cost of this will be negligible.

Sensors as the measuring devices in data acquisition process have the major role; therefore they have direct effects on the captured data. Like the other devices, sensors are not perfect and thus their outputs are not as real measurements and then the brightness values are erroneous. Sensors specifications and functionality define the ultimate raw brightness values and, sensing geometry (consist of sensor situation and position) defines how three-dimension scene is transformed into two-dimension image. Spatial, spectral and radiometric resolution and particularly the Point-Spread-Function (PSF) of the sensor are the most important characteristics of the sensor that must be taken into account for classification of remotely sensed images.