  High-resolution aerial imagery allows fine details in the segmentation of
farmlands. However, small objects and features introduce distortions to the
delineation of object boundaries, and larger contextual views are needed to
mitigate class confusion. In this work, we present an end-to-end trainable
network for segmenting farmlands with contour levees from high-resolution
aerial imagery. A fusion block is devised that includes multiple voting blocks
to achieve image segmentation and classification. We integrate the fusion block
with a backbone and produce both semantic predictions and segmentation slices.
The segmentation slices are used to perform majority voting on the predictions.
The network is trained to assign the most likely class label of a segment to
its pixels, learning the concept of farmlands rather than analyzing
constitutive pixels separately. We evaluate our method using images from the
National Agriculture Imagery Program. Our method achieved an average accuracy
of 94.34\%. Compared to the state-of-the-art methods, the proposed method
obtains an improvement of 6.96% and 2.63% in the F1 score on average.
