Automated Identification of Tile Lines from Remotely Sensed Data

Abstract

Although subsurface drainage provides many agronomic and environmental benefits, extensive subsurface drainage systems have important implications for surface water quality and hydrology. Due to limited information on subsurface drainage extent, it is difficult to understand the hydrology of intensively tile-drained watersheds. In order to address this problem, a methodology was developed to use image processing techniques for automated detection of tile drains from multiple dates of aerial photography at the Agronomy Center for Research and Education (ACRE), West Lafayette, Indiana. A stepwise approach was adopted to first identify potential tile-drained fields from the GIS-based analysis of land use class, soil drainage class, and surface slope using decision tree classification. Based on preliminary classification of potential tile-drained area from the decision tree classifier, a combination of image processing techniques such as directional edge enhancement filtering, density slice classification, Hough transformation, and automatic vectorization were used to identify individual tile lines from images of 1976, 1998, and 2002. Accuracy assessment of the predicted tile line maps (Hough transformed and untransformed) was accomplished by comparing the locations of predicted tile lines with the known tile lines mapped through manual digitization from historic design diagrams using both a confusion matrix approach and drainage density. Forty-eight percent of tile lines were correctly predicted for the Hough transformed map and 58% for the untransformed map based on the producer accuracy. Similarly, 73% of non-tile area was correctly predicted for Hough transformed and 68% for untransformed lines. Based on drainage density calculation, 60% of tile lines were predicted from the aerial image of 1976 and 50% from the aerial image of 2002 for both techniques, while 72% of tile lines were predicted from the aerial image of 1998 for untransformed and 50% for Hough transformed lines. The Hough transformation provided the best results in producing a map without discontinuity between lines. The overall performance of the image processing techniques used in this study shows that these techniques can be successfully applied to identify tile lines from aerial photographs over a large area.