final 2024_Remote_Sensing

2024_Remote_Sensing/maxlike.py

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+import rasterio
+import numpy as np
+from shapely.geometry import mapping
+from rasterio.features import geometry_mask
+import geopandas as gpd
+
+# Step 1: Load Raster Data
+def load_raster(raster_path):
+    with rasterio.open(raster_path) as src:
+        bands = [src.read(i) for i in range(1, src.count + 1)]
+        profile = src.profile
+    return np.stack(bands, axis=-1), profile
+
+# Step 2: Extract Training Data
+def extract_training_data(raster, training_shapefile, profile):
+    training_labels = gpd.read_file(training_shapefile)
+    
+    class_data = {}
+    for _, row in training_labels.iterrows():
+        class_id = row['class_id']
+        geom = [mapping(row['geometry'])]
+        
+        mask = geometry_mask(
+            geom, transform=profile['transform'], invert=True, out_shape=(profile['height'], profile['width'])
+        )
+        
+        masked_pixels = raster[mask]
+        if class_id not in class_data:
+            class_data[class_id] = []
+        class_data[class_id].append(masked_pixels)
+
+    # Aggregate class data
+    for class_id in class_data.keys():
+        class_data[class_id] = np.vstack(class_data[class_id])
+    
+    return class_data
+
+# Step 3: Calculate Mean and Covariance for Each Class
+def compute_statistics(class_data):
+    statistics = {}
+    for class_id, pixels in class_data.items():
+        mean_vector = np.mean(pixels, axis=0)
+        covariance_matrix = np.cov(pixels, rowvar=False)
+        statistics[class_id] = {
+            "mean": mean_vector,
+            "covariance": covariance_matrix
+        }
+    return statistics
+
+# Step 4: Maximum Likelihood Calculation
+def calculate_likelihood(pixel, mean, covariance):
+    # Multivariate normal distribution likelihood
+    dim = len(pixel)
+    det_cov = np.linalg.det(covariance)
+    inv_cov = np.linalg.inv(covariance)
+    
+    diff = pixel - mean
+    exponent = -0.5 * np.dot(diff.T, np.dot(inv_cov, diff))
+    likelihood = (1.0 / ((2 * np.pi) ** (dim / 2) * (det_cov ** 0.5))) * np.exp(exponent)
+    return likelihood
+
+def classify_with_maximum_likelihood(raster, statistics):
+    rows, cols, bands = raster.shape
+    classified_image = np.full((rows, cols), fill_value=255, dtype=np.uint8)  # 255 as nodata
+
+    for i in range(rows):
+        for j in range(cols):
+            pixel_spectrum = raster[i, j, :]
+            likelihoods = {
+                class_id: calculate_likelihood(pixel_spectrum, stats["mean"], stats["covariance"])
+                for class_id, stats in statistics.items()
+            }
+            classified_image[i, j] = max(likelihoods, key=likelihoods.get)  # Class with maximum likelihood
+
+    return classified_image
+
+# Step 5: Save Classified Image
+def save_classified_image(classified_image, profile, output_path):
+    profile.update(count=1, dtype=rasterio.uint8, nodata=255)
+    with rasterio.open(output_path, "w", **profile) as dst:
+        dst.write(classified_image, 1)
+
+# Main Execution
+if __name__ == "__main__":
+    # File paths
+    raster_path = "stacked/stacked.tif"
+    training_shapefile = "training/training.shp"
+    output_path = "classified/maxlike.tif"
+
+    # Load raster and metadata
+    raster, profile = load_raster(raster_path)
+
+    # Extract training data
+    print("Extracting training data...")
+    class_data = extract_training_data(raster, training_shapefile, profile)
+
+    # Compute class statistics
+    print("Computing class statistics...")
+    statistics = compute_statistics(class_data)
+
+    # Classify raster using Maximum Likelihood
+    print("Classifying with Maximum Likelihood...")
+    classified_image = classify_with_maximum_likelihood(raster, statistics)
+
+    # Save the classified image
+    save_classified_image(classified_image, profile, output_path)
+    print(f"Classified image saved to {output_path}")