Deep Learning Network Optimization for Analysis and Classification of High Band Images

Examination and categorization of high-band pictures are used to describe the process of analysing and classifying photos that have been taken in many bands. Deep learning networks are known for their capacity to extract intricate information from images with a high bandwidth. The novelty lies in the integration of adaptive motion optimization, spectral-spatial transformer for categorization, and CNN-based feature extraction, enhancing high-band picture search efficiency and accuracy. The three primary parts of the technique are adaptive motion for optimization, spectral-spatial transformer for categorization, and CNN-based feature extraction. Initially, hierarchical characteristics from high-band pictures using a CNN. The CNN method enables precise feature representation and does a good job of matching the image's high and low features. This transformer module modifies the spectral and spatial properties of pictures intended for usage, enabling more careful categorization. This method performs better when processing complicated and variable picture data by integrating spectral and spatial information. Additionally, it is preferable to incorporate adaptive motion algorithms into offering the deep learning network training set. During training, this optimization technique dynamically modifies the motion parameter for quicker convergence and better generalization performance. The usefulness of the suggested strategy is demonstrated by researchers through numerous implementations on real-world high-band picture datasets. The challenges of hyperspectral imaging (HSI) classification, driven by high dimensionality and complex spectral-spatial relationships, demand innovative solutions. Current methodologies, including CNNs and transformer-based networks, suffer from resource demands and interpretability issues, necessitating exploration of combined approaches for enhanced accuracy. In high-band image evaluation and classification applications, the approach delivers state-of-the-art performance and python-implemented model has a 97.8% accuracy rate exceeding previous methods.

@article{Sundararajan2024,
  title     = {Deep Learning Network Optimization for Analysis and Classification of High Band Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Manju Sundararajan and S. J Grace Shoba and Y. Rajesh Babu and P N S Lakshmi},
  doi       = {10.14569/IJACSA.2024.0150471},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150471}
}