wanggynpuer / 3D-RCNet

3D-RCNet: A 3D Relational Convolutional Network for Hyperspectral Image Classification

3D-RCNet:Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image Classification

Haizhao Jing, Liuwei Wan, Xizhe Xue, Haokui Zhang, Ying Li,

The 3D-RCNet framework

Fig1. The 3D-RCNet framework proposed by us, and the framework uses four stages of blocks for feature extraction at different depths on HSI data

Comparison of the three methods

**Fig2. Comparison of the three methods, the total MACs required by each method with the same input. (a) is 3D-ConvBlock,(b) is Self-attention, and (c) is our proposed 3D-RCBlock. **

Directory and File Structure

./                                            # current (project) directory
│
├── assets									  # figures and tables 
│
├── data/                                     # Files to be processed in the dataset
│   └── HSI_datasets/
│       ├── data_h5/
│       └── samples/
├── data_preprocess/
│   ├── data_list/                            # The preprocessed data is placed in the data_list folder.
		├──Indian_pines_split.txt
│   ├── functions_for_samples_extraction.py
│   ├── mat_2_h5.py                           # Dataset format conversion
│   └── preprocess.py                         # Preprocessing the dataset
└── training/
    ├── models/
    ├── functions_for_evaluating.py
    ├── functions_for_training.py
    ├── get_cls_map.py                        # Generating pseudocolored synthesized images
    └── main_cv_paper.py

🔥🔥🔥Note: The Indian_pines.txt, Indian_pines_test.txt, and Indian_pines_train.txt files generated in the data_list directory are created by executing mat_2_h5.py and preprocessing.py in sequence.🔥🔥🔥

The data folder contains the datasets to be processed

data_preprocess folder:

The data_list folder contains preprocessed data.

• mat_2_h5.py: Dataset format conversion
• preprocess.py: Data preprocessing
  • functions_for_samples_extraction.py

trainingfolder:

The models folder contains our proposed 3D-RCNet.

• get_cls_map.py: Generate pseudo-color composite images
• main_cv_paper.py: Training script
  • functions_for_training.py
  • functions_for_evaluating.py

Environment Setup and Installation

python: 3.11

NOTE: Latest PyTorch requires Python 3.8 or later.