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.