movan58 / cdfs-cascl-2024

CDFS-CASCL-2024

This is a code demo for the paper "Cross-Domain Few-shot Hyperspectral Image Classification with Cross-Modal Alignment and Supervised Contrastive Learning".

Requirements

• CUDA = 12.2

• python = 3.9.18

• torch = 1.11.0+cu113

• transformers = 4.30.2

• sklearn = 0.0.post9

• numpy = 1.26.0

Datasets

• source domain dataset

  • Chikusei

• target domain datasets

  • Indian Pines
  • Houston
  • Salinas
  • WHU-Hi-LongKou

You can download the source and target datasets mentioned above at https://pan.baidu.com/s/1wo9xj85YaT3JGogVyJKZTQ?pwd=5lkl, and move to folder datasets. In particular, for the source dataset Chikusei, you can choose to download it in mat format, and then use the utils/chikusei_imdb_128.py file to process it to get the patch size you want, or directly use the preprocessed source dataset Chikusei_imdb_128_7_7.pickle with a patch size of 7 $\times$ 7.

An example datasets folder has the following structure:

datasets
├── Chikusei_imdb_128_7_7.pickle
├── Chikusei_raw_mat
│   ├── HyperspecVNIR_Chikusei_20140729.mat
│   └── HyperspecVNIR_Chikusei_20140729_Ground_Truth.mat
├── IP
│   ├── indian_pines_corrected.mat
│   └── indian_pines_gt.mat
├── Houston
│   ├── data.mat
│   ├── mask_train.mat
│   └── mask_test.mat
├── salinas
│   ├── salinas_corrected.mat
│   └── salinas_gt.mat
└── WHU-Hi-LongKou
    ├── WHU_Hi_LongKou.mat
    └── WHU_Hi_LongKou_gt.mat

Pretrain model

You can download the pre-trained model of Base Bert, bert-base-uncased, at https://pan.baidu.com/s/1C6qExEcVd3foNtLcn7PKFw?pwd=enda, and move to folder pretrain-model.

An example pretrain-model folder has the following structure:

pretrain-model
└── bert-base-uncased
    ├── config.json
    ├── pytorch_model.bin
    ├── tokenizer.json
    ├── tokenizer_config.json
    └── vocab.txt

Usage

1. Download the required source and target datasets and move to folder datasets.

• If you down the source domain dataset (Chikusei) in mat format, you need to run the script Chikusei_imdb_128.py to generate preprocessed source domain data.
• If you downloaded Chikusei_imdb_128_7_7.pickle, move it directly to the corresponding dataset directory.

2. Download the required Base Bert pre-trained model and move to folder pretrain-model.
3. Run train.py.