Comparison of manual and automated shadow detection on satellite imagery for agricultural land delineation

Agnieszka Tarko*, Sytze De Bruin, Arnold K. Bregt

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Land cover identification and area quantification are key aspects in determining support payments to farmers under the European Common Agricultural Policy. Agricultural land is monitored using the Land Parcel Identification System and visual image interpretation. However, shadows covering reference parcel boundaries can hinder effective delineation. Visual interpretation of shadows is labor intensive and subjective, while automated methods give reproducible results. In this paper we compare shadow detection on satellite imagery obtained by expert photointerpretation to a proposed automated, data-driven method. The latter automated method is a thresholding approach employing both panchromatic and multispectral imagery, where the former has a finer spatial resolution than the latter. Thresholds are determined from automatically generated training data using a risk-based approach. Comparison of the total shadow area per scene showed that more pixels were labelled as shadow by the automatic procedure than by visual interpretation. However, the union of shadow area independently identified by twelve experts on a subscene was larger than the automatically determined shadow area. The limited intersection of the shadow areas identified by the experts demonstrated that experts strongly disagreed in their interpretations. The shadow area labelled by the automated method was in between the intersection and the union of the areas interpreted by experts. Furthermore, the automated shadow detection method is reproducible and reduces the interpretation effort and skill required.
Original languageEnglish
Pages (from-to)493-502
JournalInternational Journal of applied Earth Observation and Geoinformation
Volume73
DOIs
Publication statusPublished - 2018

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