  Semantic segmentation by convolutional neural networks (CNN) has advanced the
state of the art in pixel-level classification of remote sensing images.
However, processing large images typically requires analyzing the image in
small patches, and hence features that have large spatial extent still cause
challenges in tasks such as cloud masking. To support a wider scale of spatial
features while simultaneously reducing computational requirements for large
satellite images, we propose an architecture of two cascaded CNN model
components successively processing undersampled and full resolution images. The
first component distinguishes between patches in the inner cloud area from
patches at the cloud's boundary region. For the cloud-ambiguous edge patches
requiring further segmentation, the framework then delegates computation to a
fine-grained model component. We apply the architecture to a cloud detection
dataset of complete Sentinel-2 multispectral images, approximately annotated
for minimal false negatives in a land use application. On this specific task
and data, we achieve a 16\% relative improvement in pixel accuracy over a CNN
baseline based on patching.
