Enhancing and replacing spectral information with intermediate structural inputs: A case study on impervious surface detection

Abstract This paper assessed the incorporation of road structural information in the classification process of impervious surface areas. A multi-process classification model was adopted and it consisted of an a priori classifier and an a posteriori classifier. The role of the a priori classifier was to classify the relatively simple portions of the image. This partial classification acted as the basis for the production of linear features using an iterative Radon transform. Spatial statistics derived from the linear features led to road structural intermediate inputs (RSIIs) (for example, distance to the closest segment endpoint). RSIIs were integrated with spectral information on the remaining unclassified pixels and an assessment was done to evaluate whether they would improve a binary impervious classification task. The experimental results on a 2006 Landsat ETM+ image suggested that classification accuracy improved by 8.4% for the portion of the dataset classified with the a posteriori classifier and led to an improvement of 3.2% over the entire dataset. In addition, a more challenging and wide-reaching hypothesis was tested, namely whether RSIIs could completely replace spectral information in portions of the image instead of complementing it. Exclusive use of RSIIs matched or improved classification accuracy obtained solely from spectral information, even when more than half of the validation dataset was forwarded to the a posteriori classifier. This finding offers an important contribution to the remote sensing community, since the proposed methodology handles the missing spectral information problem through exclusive analysis of the given degraded image; no external information, such as spectral information from other times and/or vector data, is needed.