In this repository, You can find the files which implement dimensionality reduction on the hyperspectral image(Indian Pines) with classification.
License: MIT License
error message: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
it happens while predicting the KNN model before the PCA, working on the original dataset.
program: indian_pines_knnx.py
error line: model.fit(X_train, y_train)
Why not use Autoencoders for dim reduction rather than standard statistical one layer technique.
Since NN are the state of the art algorithmic approach.
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