# Classification-of-Hyperspectral-Image

**Repository Path**: mantte6199/Classification-of-Hyperspectral-Image

## Basic Information

- **Project Name**: Classification-of-Hyperspectral-Image
- **Description**: 高光谱图像分类
卷积神经网络对高光谱图像印度松的分类
高光谱图像是在数百个电磁波谱带中捕获的图像。 该项目专注于开发用于高光谱图像中的土地覆盖分类的深度神经网络。 土地覆被分类是为每个像素分配代表土地覆被类型的类别标签的任务。
现有的大多数研究工作都遵循传统的模式识别范例，该范例基于复杂的手工特征的构造。 但是，鲜为人知的是哪些功能对当前问题很重要。 与这些方法相反，提出了一种基于深度学习的分类方法，该方法以自动化方式分层构造高级特征。 在该项目中，将参与卷积神经网络的开发，以对像素的光谱和空间信息进行编码，并使用多层感知器来执行分类任务。
该项目基于论文“针对超光谱数据分类的深度监督学习” 通过卷积神经网络”，由Makantasis等人撰写。
Just to clarify, my code has nothing to do with the previously mentioned paper. I refer to it, because I followed the same resoning to build my code.
仓库描述
 打开globa
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 6
- **Forks**: 1
- **Created**: 2020-10-02
- **Last Updated**: 2025-07-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Classification-of-Hyperspectral-Image
Classification of the Hyperspectral Image Indian Pines with Convolutional Neural Network


Hyperspectral images are images captured in hundrends of bands of the electromagnetic spectrum. This project is focused at the development of Deep Neural Network for landcover classification in hyperspectral images. Land-cover classification is the task of assigning to every pixel, a class label that represents the type of land-cover.

Most of the existing studies and research efforts are following the conventional pattern recognition paradigm, which is based on the construction of complex handcrafted features. However, it is rarely known which features are important for the problem at hand. In contrast to these approaches, a deep learning based classification method that hierarchically constructs high-level features in an automated way, is proposed. In this project, exploitation of a Convolutional Neural Network, is taking part, to encode pixels’ spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task.

This project is based on the paper "DEEP SUPERVISED LEARNING FOR HYPERSPECTRAL DATA CLASSIFICATION
THROUGH CONVOLUTIONAL NEURAL NETWORKS" by Makantasis et al. 

`Just to clarify, my code has nothing to do with the previously mentioned paper. I refer to it, because I followed the same resoning to build my code.`

Description of the repository

1) Open the global_variables.txt file and write the wanted windowsize, the number of the PCA components and the test train split.

2) Run the notebook "CreatetheDatasets", in order to create the Xtrain, Xtest, ytrain, ytest matrices. Matrices are saved in a numpy format.

3) Run the notebook "TrainTheModel", in order to train the model. The model is being saved including the weights.

4) Run the "Validation+ClassificationMaps", for validating the model and creating the clasification map.

# Figures


| Patch Size | Overall Accuracy |
|   :---:    | :---:            |
|   5x5      | 83%              |
|7x7         | 88%              |
| 9x9        | 94%              |
|11x11       | 95%              |



![CNN_Architecture](./images/CNN_Architecture.jpeg)