Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations

Understand

In this work we consider the application of convolutional neural networks (CNNs) for pixel-wise labeling (a.k.a., semantic segmentation) of remote sensing imagery (e.g., aerial color or hyperspectral imagery).

• Remote sensing imagery is usually stored in the form of very large images, referred to as "tiles", which are too large to be segmented directly using most CNNs and their associated hardware.
• As a result, during label inference, smaller sub-images, called "patches", are processed individually and then "stitched" (concatenated) back together to create a tile-sized label map.
• This approach suffers from computational ineffiency and can result in discontinuities at output boundaries.