The effect of different classification algorithms knowledge base in the increase the accuracy of the maps of land use

In order the rapid extraction, land use maps, remote sensing technology has been recognized as an efficient technology to provide satellite images enabling the extraction provides maps land use. Satellite remote sensing images in different temporal power modeling and monitoring changing environmental conditions that made it possible is an important step in the management of natural resources. The basic knowledge object-oriented classification the method based algorithm, an efficient the method for classification of satellite images, in addition of spectral data, satellite images, facilities for the use of environmental information and the physical and geometric characteristics of the land surface phenomena provides The present study aimed to evaluate the increase accuracy of classification utilize algorithms and fuzzy The basic knowledge maps use / land cover done. In this Study To evaluate and compare methods for objectoriented classification of satellite images using fuzzy algorithms and object-oriented methods based on the fuzzy algorithm, Alos Landsat satellite images were used to AVNIR2 sensor and Maragheh city maps and land with both of these methods have been used. The accuracy of the results of the evaluate the show that land use map produced by the the method of fuzzy The basic knowledge With an overall accuracy of 93.28 in comparison with land use map produced by the object-oriented the method using fuzzy algorithm with accuracy 88.06% of the credit is more according to the nature of this study was to compare the results to identify optimal methods are important in the production of land-use mapping And maps produced for performance agencies (such as agriculture, natural resources, etc.) are of high practical value.