  Current hyperspectral image classification assumes that a predefined
classification system is closed and complete, and there are no unknown or novel
classes in the unseen data. However, this assumption may be too strict for the
real world. Often, novel classes are overlooked when the classification system
is constructed. The closed nature forces a model to assign a label given a new
sample and may lead to overestimation of known land covers (e.g., crop area).
To tackle this issue, we propose a multitask deep learning method that
simultaneously conducts classification and reconstruction in the open world
(named MDL4OW) where unknown classes may exist. The reconstructed data are
compared with the original data; those failing to be reconstructed are
considered unknown, based on the assumption that they are not well represented
in the latent features due to the lack of labels. A threshold needs to be
defined to separate the unknown and known classes; we propose two strategies
based on the extreme value theory for few-shot and many-shot scenarios. The
proposed method was tested on real-world hyperspectral images; state-of-the-art
results were achieved, e.g., improving the overall accuracy by 4.94% for the
Salinas data. By considering the existence of unknown classes in the open
world, our method achieved more accurate hyperspectral image classification,
especially under the few-shot context.
