The paper titled "Multiple vision architectures-based hybrid network for hyperspectral image classification" has been accepted by Expert Systems with Applications (ESWA) in July 2023, and this is an official implementation for MVAHN.
Fig. 1. The skeleton of three mainstream neural networks. (a) Convolutional neural network. (b) Graph convolutional network. (c) Transformer.
Fig. 2. Overview illustration of the proposed multiple vision architectures-based hybrid network (MVAHN) for hyperspectral image classification. The yellow shaded area is convolution embedded transformer encoders, and the blue shaded area is graph convolutional module (GCM).
Torch: 1.7.0
Python: 3.7.3
Learning Rate: 0.0005
Epoch: 100
Batch Size: 100
Patch Size: 11x11
Encoder Layer: 1
Attention Header: 4
Optimizer: Adam (weight_decay=1e-4)
Scheduler: CosineAnnealingLR
Link: https://pan.baidu.com/s/1pmCMYZ7cjU8OeadBa6MwIw
Extraction code: HSIC
The above link contains six hyperspectral image datasets used in the paper.
We encourage researchers to cite our latest work.
We encourage researchers to achieve different comparative experiments (papers) within a code framework, to achieve a fair comparison.
If you encounter any problems reproducing the code, please do not hesitate to contact us.
E-mail: junjiezhang98@yeah.net