16 February 2006 Artificial neural networks and decision tree classifier performance on medium resolution ASTER data to detect gully networks in southern Italy
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Abstract
Gully erosion has the potential to cause significant land degradation, yet the scale of gully features means that changes are difficult to map. Here we describe the application of ASTER imagery, surface modelling and land cover information to detect gully erosion networks with maximum obtainable accuracy. A grey level co-occurrence matrix (GLCM) texture analysis method was applied to ASTER bands as one of the input layers. GLCM outputs were combined with geomorphological input layers such as flow accumulation, slope angle and aspect, which were derived from an ASTER-based digital elevation model (DEM). The ASTER-based DEM with 15-meter resolution was prepared from L1A. Artificial neural networks (ANN) and decision tree (DT) approaches have been used to classify input layers for five sample areas. This differentiates gullies from landscape areas with no gullies. We found that DT methods classified the image with the highest accuracy (85% overall) in comparison with the ANN.
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A. Ghaffari, G. Priestnall, M. L. Clarke, "Artificial neural networks and decision tree classifier performance on medium resolution ASTER data to detect gully networks in southern Italy", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60641Q (16 February 2006); doi: 10.1117/12.660602; https://doi.org/10.1117/12.660602
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