A python deep learning model which is super useful in dealing with hyperspectral image classification with noisy labels.

The loss function gets its code from Link

Links to relevant comparison methods are shown below.

• SSRN Spectral-spatial residualnetwork for hyperspectral image classification: A 3-D deep learning framework
• DPNLD Density Peak-based Noisy Label Detection for Hyperspectral Image Classification
• RLPA Hyperspectral image classification in the presence of noisy labels
• K-SDP Spatial density peak clustering for hyperspectral image classification with noisy labels
• 3D-CNN Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network

This code is compatible with Python 3.7.6.

It is based on PyTorch 1.5.1 and torchvision 0.6.1

If a GPU is detected, CUDA 10.0+ is used to boost training.

Default settings are capsuled in main.py already. If tested with default parameters, just run main.py like

python3 main.py

Currently, we only support a few adjustable hyper-parameters:

• batch_size: batch size, default to be 16
• max_iter: max training epochs, default to be 100
• iters: repetition experments' number, default to be 10
• lr: learning rate, default to be 0.001.

Example to run with personal settings:

python3 main.py --batch_size 32 --max_iter 200

The code in SDP is for paper "Spatial Density Peak Clustering of Hyperspectral Images with Noise Labels".

An example dataset folder has the following structure:

datasets
├── KSC
│   ├── KSC.mat
│   ├── KSC_gt.mat
├── salinas
│   ├── salinas_corrected.mat
│   └── salinas_gt.mat
└── paviaU
    ├── paviaU_gt.mat
    └── paviaU.mat