# SHIP

**Repository Path**: mantte6199/SHIP

## Basic Information

- **Project Name**: SHIP
- **Description**: 监督的高光谱图像分类包（SHIP）: 各种数据集

这是一个开放式软件包（称为 SHIP ），用于监督高光谱图像分类任务。 此外，该存储库保证您可以重现论文中报告的结果：

    曹向勇，徐宗本，孟德玉，基于鲁棒低秩特征提取和马尔可夫随机场的光谱空间高光谱图像分类，遥感.2019，11（13），1565。

如果使用此代码，请在工作中引用该论文。 
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 9
- **Forks**: 0
- **Created**: 2020-10-01
- **Last Updated**: 2025-04-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Supervised Hyperspectral Image Classification Package (SHIP)

This is an open package (called **SHIP**) for supervised hyperspectral image classification task. Besides, this repository guarantees you to reproduce the results reported in the paper:
 - [Xiangyong Cao, Zongben Xu and Deyu Meng, Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field, Remote Sens. 2019, 11(13), 1565.](https://www.mdpi.com/2072-4292/11/13/1565) 

If you use this code, pleae cite this paper in your work. 

## Setup
### Install Dependencies
If you were using Ubuntu, simply type the following commands in your terminal to install dependencies: 

        python setup.py

### Download and prepare the datasets
Download the datasets used in the paper from the following link:
 - [https://pan.baidu.com/s/1mtAJ73RU8pb38GuIfe7FLQ](https://pan.baidu.com/s/1mtAJ73RU8pb38GuIfe7FLQ)
 - Code: g8bg
 
After downloading the datasets file, put it in the main directory of SHIP file.

## Reproducing the results

1. To choose the best classifier for one given feature (also reproduce the result in Exp 4.1), execute

        python demo_Exp1.py

   According to your needs, many parameters can be set in demo_Exp1.py, such as dataset, feature, classifieris, train_size, repeat_num, model_selection, isdraw. More detailed comments of these parameters can be found in SHSIC function. 
   
   This package supports 7 datasets, 6 features (5 classical features and 1 deep feature), 9 classifiers, model selection for each classifier, post-processing classification map and drawing classification map for each method . 

2. To reproduce the results in Exp 4.3, execute

        python demo_Exp2.py


   According to your needs, many parameters can be set in demo_Exp2.py.


## Contact:
This package is still developing and this is the first version. In the next step, we prepare to embed the feature extraction methods into this package, thus it can implement the feature extraction (this package only provide some pre-extracted features by some offline code). 

For the package of this version, we hope more reserachers in this field can provide your extracted feature data to me. Welcome to contact me (Xiangyong Cao:   caoxiangyong45@gmail.com  /  caoxiangyong@mail.xjtu.edu.cn).