Add preprocessing and PCA-based hyperspectral classification example

README.rst

@@ -72,3 +72,10 @@ Using SPy interactively with its visualization capabilities requires `IPython` a
 several other packages (depending on the features used). See the
 `web site <http://spectralpython.net>`_ for details.
 
+## Custom Experiments
+
+Added a preprocessing and PCA-based classification script under `scripts/`
+to demonstrate hyperspectral image normalization, dimensionality reduction,
+and classical ML-based classification using Spectral Python.
+
+

scripts/preprocess_and_classify.py

@@ -0,0 +1,63 @@
+"""
+Preprocessing and PCA-based classification example
+using Spectral Python library.
+"""
+
+import spectral
+from spectral import open_image
+import numpy as np
+from sklearn.decomposition import PCA
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+import matplotlib.pyplot as plt
+
+# ---- Load sample hyperspectral image ----
+# NOTE: Replace path with actual .hdr file when running
+# Example datasets are mentioned in Spectral Python docs
+# OPTION 1: Synthetic hyperspectral-like data (used for pipeline validation)
+img = np.random.rand(50, 50, 20)
+
+# OPTION 2: Real hyperspectral data (uncomment when dataset is available)
+# img = open_image('data/example/your_dataset.hdr').load()
+
+# NOTE:
+# Synthetic data is used by default to validate the preprocessing and
+# classification pipeline. Real hyperspectral datasets can be loaded
+# by uncommenting the line below and providing a valid .hdr file path.
+
+# ---- Basic preprocessing ----
+# Normalize data
+img = (img - np.mean(img)) / np.std(img)
+
+# ---- Reshape for ML ----
+h, w, bands = img.shape
+pixels = img.reshape(-1, bands)
+
+# ---- Dimensionality reduction ----
+pca = PCA(n_components=10)
+pixels_pca = pca.fit_transform(pixels)
+
+# ---- Dummy labels (placeholder) ----
+# In real datasets, labels come from ground truth files
+labels = np.random.randint(0, 2, size=pixels_pca.shape[0])
+
+# ---- Train-test split ----
+X_train, X_test, y_train, y_test = train_test_split(
+    pixels_pca, labels, test_size=0.2, random_state=42
+)
+
+# ---- Train classifier ----
+clf = SVC(kernel='rbf')
+clf.fit(X_train, y_train)
+
+# ---- Evaluate ----
+y_pred = clf.predict(X_test)
+acc = accuracy_score(y_test, y_pred)
+print(f"Classification Accuracy: {acc:.4f}")
+
+# ---- Visualization ----
+plt.imshow(img[:, :, :3])
+plt.title("RGB Composite (First 3 Bands)")
+plt.axis('off')
+plt.show()