Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel

New sensors and new technologies for remotely sensed data acquisition have proven useful for mapping urban environments. In this paper, a new dataset from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spaceborne sensor was used with support vector machine (SVM)-based algorithms for classification processing. A case study of Beer-Sheva, Israel demonstrates that ASTER data are suitable for urban studies. The classification results also show that the approach based on SVM has high performance in convergence, speed, and accuracy of training and classifying. Field validation shows that the classification is reliable for urban studies with high classification precision and little confusion (88.6% average overall accuracy and 9.6% average omission error for 15-m resolution image classification vs. 89.9% average overall accuracy and 11.2% average omission error for 30-m resolution image classification). For the 30-m multispectral data, there are only five classes vs. six for the 15-m visible spectrum data. Despite the additional classes retrieved from the visible data, the short-wave infrared (SWIR) data provides the accuracy in differentiating some of the confused classes.