Indian Pines Dataset: Dimensionality Reduction with PCA

import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.io as io\n\n# Importing the dataset from Matlab format\ndataset = io.loadmat('indianpines_dataset.mat')\nnumber_of_bands = int(dataset['number_of_bands'])\nnumber_of_rows = int(dataset['number_of_rows'])\nnumber_of_columns = int(dataset['number_of_columns'])\npixels = np.transpose(dataset['pixels'])\n\n# Importing the Groundtruth\ngroundtruth = io.loadmat('indianpines_gt.mat')\ngt = np.transpose(groundtruth['pixels'])\n\n# Feature Scaling\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\npixels = sc.fit_transform(pixels)\n\n# Applying Dimensionality Reduction\n\nfrom sklearn.decomposition import PCA\npca = PCA(n_components=10)\npixels = pca.fit_transform(pixels)