Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

•Landsat TM and Landsat OLI are compared in the performance of landcover classification.•Commercial off-the-shelf (COTS) object based and pixel based methods have been applied.•The study area is a fragmented urban area in East Attica, Greece.•Pixel based classification by using SVM algorithm has the best results.•Landsat OLI has better classification accuracy comparing to Landsat TM.

An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification results from Landsat OLI provide more accurate results comparing to the Landsat TM. Object-based classifications produced a more uniform result, but suffer from the absorption of small rare classes into large homogenous areas, as a consequence of the segmentation, merging and the spatial parameters in the spatial resolution (30 m) of Landsat images. Based exclusively on the overall accuracy reports, the SVM pixel-based classification from Landsat 8 proved to be the most accurate for the purpose of mapping urban land cover, using medium spatial resolution imagery.

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