Supervised and unsupervised satellite image classifications have progressed greatly in recent years. However, discrimination difficulties still remain among classes that directly affecting data extraction and surface mapping accuracy. The Ouargla region in southeastern Algeria is intersected by wadis, where direct communication between the shallow groundwater table and these dry, overlying ephemeral stream beds exists. Underflowing groundwater exfiltrates into low-lying aeolian blowouts or endorheic basins forming oases, chotts, and sebkhas, commonly known as saline wetlands. These wetlands are becoming increasingly vulnerable to anthropogenic stress, resulting in significant water degradation. Wetland microclimates are very important to arid regions, as they promote oasis ecosystem sustainability and preservation. High water salinity in these ecosystems, however, directly affects flourishing habitat and undermines successful desert oasis development. The objective of this work is to choose the best classification method to identify saline wetlands by comparison between the different results of land use mapping within the Ouargla basin. Landsat ETM+ (2000) satellite imagery, using visual analysis with colored compositions, has identified various forms of saline wetlands in the Ouargla region desert environment in southeast Algeria. The results show that supervised classification is validated in the identification of Saharan saline wetlands, and that support vector machine (SVM) algorithm presents the best overall accuracy.