New Framework for Hyperspectral Image Classification
Author Information
Author(s): Phaneendra Kumar B. L. N., Vaddi Radhesyam, Manoharan Prabukumar, Agilandeeswari L., Sangeetha V.
Primary Institution: Vellore Institute of Technology, Vellore
Hypothesis
Can a new band selection framework improve classification accuracy in hyperspectral remote sensing images?
Conclusion
The proposed method achieved high classification accuracy of 99.92% on Indian Pines, 99.94% on Salinas, and 97.23% on KSC datasets.
Supporting Evidence
- The proposed method reported overall accuracy of 99.92% on Indian Pines dataset.
- The method achieved 99.94% accuracy on Salinas dataset.
- 97.23% accuracy was reported on the KSC dataset.
- Mean Spectral Divergence values were 42.4, 63.75, and 41.2 for the three datasets respectively.
Takeaway
This study created a new way to pick the best parts of images taken from space, helping to identify different crops very accurately.
Methodology
The study used a dual partitioning strategy for band selection and a Convolutional Neural Network for classification.
Limitations
The method requires manual tuning of parameters, which may affect its adaptability.
Digital Object Identifier (DOI)
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