Hyper Spectral Image(HSI) Analysis Simplified

The repository contains the implementation of different machine learning techniques on Hyperspectral and satellite Imagery analysis. Find more articles from here.

1. Basics - This notebook fatures:

• Introduction
• Downloading HSI
• Reading the hyperspecral image.
• Visualizing the bands of the hyperspectral image.
• Visualizing ground truth of the image.
• Extracting pixels of the hyperspectral image.
• Visualizing spectral signatures of the hyperspectral image.

2. Data Analysis - This notebook fatures data anlysis of the indian pines hyperspectral image:

• Visualizing pixels of the hyperspectral image.
• Bar plot w.r.t class labels of the hyperspectral image.
• Box Plot w.r.t the class labels and bands of hyperspecral image.
• Distribution Plot w.r.t the bands of hyperspecral image.

3.Exploratory Data Analysis (EDA) on Satellite Imagery Using EarthPy

4.Dimensionality Reduction

• Check this article entitled Dimensionality Reduction in Hyperspectral Images using Python and code.

• PCA + SVM - This notebook implements the following machine learning techniques on the indian pines dataset.

  • Dimensionality Rreduction: The principal component analysis(PCA) is used to reduce the dimensions of the dataset.
  • Classifier: The support vector machine(SVM) classifier is used to classsify the pixels of the HSI with classification report and the confusion matrix, classification map of the classifier is visualized.

• Kernel PCA + SVM - This notebook implements the following machine learning techniques on the indian pines dataset.

  • Dimensionality Rreduction: The Kernel principal component analysis(PCA) with 'rbf kernel' is used to reduce the dimensionality of the dataset.
  • Classifier: The support vector machine(SVM) classifier is used to classsify the pixels of the HSI with classification report and the confusion matrix, classification map of the classifier is visualized.

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