Hyperspectral-Classification-Platform

This is a deep learning platform for hyperspectral classification by pytorch, integrated several classic HSIC models including CNN1D,A2S2KResNet,HybridSN,M3DCNN,SSUN,SSFCN SpectralFormer,VIT,GhostNet,FullyContNet,MobileNet,UNet. This is also an official implement of "SSRNet: A Lightweight Successive Spatial Rectified Network With Noncentral Positional Sampling Strategy for Hyperspectral Images Classification" and "A Multi-scale Convolutional Neural Network Based on Multilevel Wavelet Decomposition for Hyperspectral Image Classification"

Install

Firstly conda create -n myenv python==3.7 conda activate myenv conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch conda install --file requirements.txt

Usage

We used visdom for visualizing experiments, so you should run visdom.server firstly.

python -m visdom.server

You also can just run the experiment without visdom by command line parameter --VISDOM 0

python main.py --MODEL SSRNet --VISDOM 0

Examples

You can easily run different hyperspectral models by command lines. For example:

python main.py --MODEL SSRNet       --DATASET  IP  --RUNS 1 --DEVICE 0 --VISDOM 1
                       A2S2KResNet             UP
                       CNN1D                   UH
                       FullyContNet
                       GhostNet
                       HybridSN
                       M3DCNN
                       MLWBDN
                       MobileNet
                       SSFCN
                       SSUN
                       UNet
                       SFormer_px
                       SFormer_pt
                       VIT

We also provide many basic parameters for training and test such as batchsize, epoch, learning rate and so on. You can see details in param file.

Acknowledge

DotWang/FullyContNet

eecn/Hyperspectral-Classification

danfenghong/IEEE_TGRS_SpectralFormer

YonghaoXu/SSFCN

suvojit-0x55aa/A2S2K-ResNet