Semantic Segmentation of Multispectral Images via Linear Compression of Bands: An Experiment Using RIT-18

Yuanzhi Cai, Lei Fan*, Cheng Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Semantic segmentation of remotely sensed imagery is a basic task for many applications, such as forest monitoring, cloud detection, and land-use planning. Many state-of-the-art networks used for this task are based on RGB image datasets and, as such, prefer three-band images as their input data. However, many remotely sensed images contain more than three spectral bands. Although it is technically possible to feed multispectral images directly to those networks, poor segmentation accuracy was often obtained. To overcome this issue, the current image dimension reduction methods are either to use feature extraction or to select an optimal combination of three bands through different trial processes. However, it is well understood that the former is often comparatively less effective, because it is not optimized towards segmentation accuracy, while the latter is less efficient due to repeated trial selections of three bands for the optimal combination. Therefore, it is meaningful to explore alternative methods that can utilize multiple spectral bands efficiently in the state-of-the-art networks for semantic segmentation of similar accuracy as the trial selection approach. In this study, a hot-swappable stem structure (LC-Net) is proposed to linearly compress the input bands to fit the input preference of typical networks. For the three commonly used network structures tested on the RIT-18 dataset (having six spectral bands), the approach proposed was found to be an equivalently effective but much more efficient alternative to the trial selection approach.

Original languageEnglish
Article number2673
JournalRemote Sensing
Issue number11
Publication statusPublished - 1 Jun 2022


  • LC-Net
  • convolutional neural network
  • multispectral image
  • semantic segmentation
  • transfer learning
  • transformer


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