Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping

作者：Claudio Persello, Valentyn Tolpekin, John Ray Bergado, R.A. de By · 发表于：Remote Sensing of Environment · 年份：2019 · DOI：10.1016/j.rse.2019.111253 · 被引用次数：210 · 研究领域：Land Use and Ecosystem Services、Remote-Sensing Image Classification、Smart Agriculture and AI

Accurate spatial information of agricultural fields in smallholder farms is important for providing actionable information to farmers, managers, and policymakers. Very High Resolution (VHR) satellite images can capture such information. However, the automated delineation of fields in smallholder farms is a challenging task because of their small size, irregular shape and the use of mixed-cropping systems, which make their boundaries vaguely defined. Physical edges between smallholder fields are often indistinct in satellite imagery and contours need to be identified by considering the transition of the complex textural pattern between fields. In these circumstances, standard edge-detection algorithms fail to extract accurate boundaries. This article introduces a strategy to detect field boundaries using a fully convolutional network in combination with a globalisation and grouping algorithm. The convolutional network using an encoder-decoder structure is capable of learning complex spatial-contextual features from the image and accurately detects sparse field contours. A hierarchical segmentation is derived from the contours using the oriented watershed transform and by iteratively merging adjacent regions based on the average strength of their common boundary. Finally, field segments are obtained by adopting a combinatorial grouping algorithm exploiting the information of the segmentation hierarchy. An extensive experimental analysis is performed in two study areas in Nigeri...