  High-resolution image segmentation remains challenging and error-prone due to
the enormous size of intermediate feature maps. Conventional methods avoid this
problem by using patch based approaches where each patch is segmented
independently. However, independent patch segmentation induces errors,
particularly at the patch boundary due to the lack of contextual information in
very high-resolution images where the patch size is much smaller compared to
the full image. To overcome these limitations, in this paper, we propose a
novel framework to segment a particular patch by incorporating contextual
information from its neighboring patches. This allows the segmentation network
to see the target patch with a wider field of view without the need of larger
feature maps. Comparative analysis from a number of experiments shows that our
proposed framework is able to segment high resolution images with significantly
improved mean Intersection over Union and overall accuracy.
