Two-stage subpixel impervious surface coverage estimation: comparing classification and regression trees and artificial neural networks

Katarzyna Bernat; Wojciech Drzewiecki

doi:10.1117/12.2067308

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23 October 2014 Two-stage subpixel impervious surface coverage estimation: comparing classification and regression trees and artificial neural networks

Katarzyna Bernat, Wojciech Drzewiecki

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Proceedings Volume 9244, Image and Signal Processing for Remote Sensing XX; 92441I (2014) https://doi.org/10.1117/12.2067308
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

Abstract

The paper presents accuracy comparison of subpixel classification based on medium resolution Landsat images, performed using machine learning algorithms built on decision and regression trees method (C.5.0/Cubist and Random Forest) and artificial neural networks. The aim of the study was to obtain the pattern of percentage impervious surface coverage, valid for the period of 2009-2010. Imperviousness index map generation was a two-stage procedure. The first step was classification, which divided the study area into categories: i) completely permeable (imperviousness index less than 1%) and ii) fully or partially impervious areas. For pixels classified as impervious, the percentage of impervious surface coverage in pixel area was estimated. The root mean square errors (RMS) of determination of the percentage of the impervious surfaces within a single pixel were 11.0% for C.5.0/Cubist method, 11.3% for Random Forest method and 12.6% using artificial neural networks. The introduction of the initial hard classification into completely permeable areas (with imperviousness index <1%) and impervious areas, allowed to improve the accuracy of imperviousness index estimation on poorly urbanized areas covering large areas of the Dobczyce Reservoir catchment. The effect is also visible on final imperviousness index maps.

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Katarzyna Bernat and Wojciech Drzewiecki "Two-stage subpixel impervious surface coverage estimation: comparing classification and regression trees and artificial neural networks", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 92441I (23 October 2014); https://doi.org/10.1117/12.2067308

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