  Recent advances in computer vision using deep learning with RGB imagery
(e.g., object recognition and detection) have been made possible thanks to the
development of large annotated RGB image datasets. In contrast, multispectral
image (MSI) and hyperspectral image (HSI) datasets contain far fewer labeled
images, in part due to the wide variety of sensors used. These annotations are
especially limited for semantic segmentation, or pixel-wise classification, of
remote sensing imagery because it is labor intensive to generate image
annotations. Low-shot learning algorithms can make effective inferences despite
smaller amounts of annotated data. In this paper, we study low-shot learning
using self-taught feature learning for semantic segmentation. We introduce 1)
an improved self-taught feature learning framework for HSI and MSI data and 2)
a semi-supervised classification algorithm. When these are combined, they
achieve state-of-the-art performance on remote sensing datasets that have
little annotated training data available. These low-shot learning frameworks
will reduce the manual image annotation burden and improve semantic
segmentation performance for remote sensing imagery.
