  High-resolution hyperspectral images (HSIs) contain the response of each
pixel in different spectral bands, which can be used to effectively distinguish
various objects in complex scenes. While HSI cameras have become low cost,
algorithms based on it has not been well exploited. In this paper, we focus on
a novel topic, semi-supervised semantic segmentation in cityscapes using
HSIs.It is based on the idea that high-resolution HSIs in city scenes contain
rich spectral information, which can be easily associated to semantics without
manual labeling. Therefore, it enables low cost, highly reliable semantic
segmentation in complex scenes.Specifically, in this paper, we introduce a
semi-supervised HSI semantic segmentation network, which utilizes spectral
information to improve the coarse labels to a finer degree.The experimental
results show that our method can obtain highly competitive labels and even have
higher edge fineness than artificial fine labels in some classes. At the same
time, the results also show that the optimized labels can effectively improve
the effect of semantic segmentation. The combination of HSIs and semantic
segmentation proves that HSIs have great potential in high-level visual tasks.
