  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 have not been well exploited. In this paper, we focus on
a novel topic, weakly-supervised semantic segmentation in cityscape via 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 theoretically
analyze the HSIs and introduce a weakly-supervised HSI semantic segmentation
framework, 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
refined 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.
