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Hyper Spectral Image(HSI) Analysis Simplified

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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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