Hyperspectral Image Segmentation using End-to-End CNN Architecture with built-in Feature Compressor for UAV Systems

Hyperspectral image segmentation is an important task for geographical surveying. Real-time processing of this operation is especially important for sensors mounted on-board Unmanned Aerial Vehicles in the context of visual servoing, landmarks recognition and data compression for efficient storage and transmission. To this end, this paper proposes a machine learning approach for segmentation using an efficient Convolutional Neural Network (CNN) which incorporates a feature compressor and a subsequent segmentation module based on 3D convolution operations. The experimental results demonstrate that the proposed approach gives segmentation accuracy at par with conventional approaches based on Principal Component Analysis (PCA) to reduce the feature dimensionality. Moreover, the proposed network is at least 35% faster than the conventional CNN-based approaches using 3D convolutions.

@article{Bilal2022,
  title     = {Hyperspectral Image Segmentation using End-to-End CNN Architecture with built-in Feature Compressor for UAV Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Bilal and Khalid Munawar and Muhammad Shafique Shaikh and Ubaid M. Al-Saggaf and Belkacem Kada},
  doi       = {10.14569/IJACSA.2022.0131202},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131202}
}