Hyperspectral Image Classification Using Deep Matrix Capsules
Deep Matrix Capsules is based on the concept of matrix capsules with Expectation-Maximization (EM) routing algorithm, specifically designed to accommodate the nuances in the HSI data to efficiently exploit spectral-spatial relationships with reduced computational complexity.
Deep Matrix Capsules Architecture for HSI Classification
Fig: The Indian Pines dataset classification result (Overall Accuracy 99.93%) of Deep Matrix Capsules using 50% samples for training. (a) RGB Composition. (b) Ground-truth classification Map. (c) Classification map corresponding to Deep Matrix Capsules. (d) Class legend.
Fig: The Salinas Scene dataset classification result (Overall Accuracy 100.00%) of Deep Matrix Capsules using 50% samples for training. (a) RGB Composition. (b) Ground-truth classification Map. (c) Classification map corresponding to Deep Matrix Capsules. (d) Class legend.
Fig: The University of Pavia dataset classification result (Overall Accuracy 99.99%) of Deep Matrix Capsules using 50% samples for training. (a) RGB Composition. (b) Ground-truth classification Map. (c) Classification map corresponding to Deep Matrix Capsules. (d) Class legend.
@INPROCEEDINGS{10028853,
author={Ravikumar, Anirudh and Rohit, P N and Nair, Mydhili K and Bhatia, Vimal},
booktitle={2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI)},
title={Hyperspectral Image Classification Using Deep Matrix Capsules},
year={2022},
volume={01},
number={},
pages={1-7},
doi={10.1109/ICDSAAI55433.2022.10028853}}
The following repositories were used for this work
- Matrix Capsules using PyTorch
- HybridSN
- HSI - Traditional to Deep Models
- SpectralNET
- HSI Classification
- Double-Branch Dual-Attention Mechanism Network
Copyright (c) 2023 Rohit P N and Anirudh Ravikumar. Released under the MIT License. See LICENSE for details.