The use of remote sensing in forest management and agriculture is becoming more prominent. The rapid development of technology allowed the emergence of database suitable for precision application in addition to the previously used low-resolution and low data content images. The high resolution, hyperspectral images are not only suitable for separating the different land use categories and vegetation types but also for examining the soil characteristics and biophysical features of plants (Blackburn and Steel, 1999; Condit, 1970). We processed a multispectral satellite image (Landsat 7 ETM+) and a hypespectral areal image (DAIS 7915) about a farm on the plains and evaluated the different image classification methods. During our examinations, we examined the geometrical and radiometrical characteristics of images first, then assigning the training areas, we determined the spectral characteristics of land use categories. We performed a multispectral analysis for checking land use, where we compared controlled and uncontrolled classification systems to check their reliability. We used areal and spectral reductions to make the classifications more accurate and to reduce the length of calculations.