I'm working with multi-temporal datasets from Landsat (Landsat 8 & Landsat 7 & Landsat 5) and I tried to extract impervious areas, more exactly urban areas, but I can't obtained results very close to reality (accuracy was approximately 80 % by using reference point in Google Earth). I used supervised classification: SAM & Maximum Likelihood, then logical classification (between SAVI + MNDWI + NDBI) but I can't obtained very good results. I used bands: Red, Green, Blue, NIR, MIR, SWIR 2. How I can extract urban areas from different Landsat image? What methods would you me recommend ?

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    Welcome to GIS.SE! Can you edit your question to include which software suites you would like to use? – Paul Jun 14 '14 at 15:00

Classifying urban areas from Landsat data is a common practice and usually yields accurate results. To improve your accuracy I would reassess your training data. As a rule-of-thumb: 1) the more samples the better, 2) samples should be distributed evenly throughout the scene. There are numerous studies on just this topic:

  1. Extraction of urban built-up land features from Landsat imagery...
  2. An assessment of landsat MSS and TM data for urban and near-urban land-cover digital classification
  3. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing
  4. Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana
  5. Use of impervious surface in urban land-use classification
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