  Semantic segmentation is a challenging task since it requires excessively
more low-level spatial information of the image compared to other computer
vision problems. The accuracy of pixel-level classification can be affected by
many factors, such as imaging limitations and the ambiguity of object
boundaries in an image. Conventional methods exploit three-channel RGB images
captured in the visible spectrum with deep neural networks (DNN). Thermal
images can significantly contribute during the segmentation since thermal
imaging cameras are capable of capturing details despite the weather and
illumination conditions. Using infrared spectrum in semantic segmentation has
many real-world use cases, such as autonomous driving, medical imaging,
agriculture, defense industry, etc. Due to this wide range of use cases,
designing accurate semantic segmentation algorithms with the help of infrared
spectrum is an important challenge. One approach is to use both visible and
infrared spectrum images as inputs. These methods can accomplish higher
accuracy due to enriched input information, with the cost of extra effort for
the alignment and processing of multiple inputs. Another approach is to use
only thermal images, enabling less hardware cost for smaller use cases. Even
though there are multiple surveys on semantic segmentation methods, the
literature lacks a comprehensive survey centered explicitly around semantic
segmentation using infrared spectrum. This work aims to fill this gap by
presenting algorithms in the literature and categorizing them by their input
images.
