final 2024_Remote_Sensing

2024_Remote_Sensing/sam.py

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+import rasterio
+import numpy as np
+from shapely.geometry import mapping
+from rasterio.features import geometry_mask
+
+# 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 Reference Spectra
+def extract_reference_spectra(raster, training_shapefile, profile):
+    import geopandas as gpd
+    training_labels = gpd.read_file(training_shapefile)
+    
+    reference_spectra = {}
+    
+    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]
+        mean_spectrum = np.mean(masked_pixels, axis=0)
+        reference_spectra[class_id] = mean_spectrum
+
+    return reference_spectra
+
+# Step 3: Spectral Angle Mapping
+def spectral_angle(pixel_spectrum, reference_spectrum):
+    # Avoid zero vectors
+    pixel_norm = np.linalg.norm(pixel_spectrum)
+    reference_norm = np.linalg.norm(reference_spectrum)
+    
+    if pixel_norm == 0 or reference_norm == 0:
+        return np.pi  # Maximum angle (180 degrees)
+
+    numerator = np.dot(pixel_spectrum, reference_spectrum)
+    denominator = pixel_norm * reference_norm
+    
+    # Clip to avoid numerical issues with arccos
+    cosine_similarity = numerator / denominator
+    cosine_similarity = np.clip(cosine_similarity, -1.0, 1.0)
+    
+    angle = np.arccos(cosine_similarity)
+    return angle
+
+
+def classify_with_sam(raster, reference_spectra):
+    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, :]
+            angles = {class_id: spectral_angle(pixel_spectrum, ref_spectrum) 
+                      for class_id, ref_spectrum in reference_spectra.items()}
+            classified_image[i, j] = min(angles, key=angles.get)  # Class with smallest angle
+
+    return classified_image
+
+# Step 4: 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/sam.tif"
+
+    # Load raster and metadata
+    raster, profile = load_raster(raster_path)
+
+    # Extract reference spectra
+    print("Extracting reference spectra...")
+    reference_spectra = extract_reference_spectra(raster, training_shapefile, profile)
+
+    # Classify raster using SAM
+    print("Classifying with SAM...")
+    classified_image = classify_with_sam(raster, reference_spectra)
+
+    # Save the classified image
+    save_classified_image(classified_image, profile, output_path)
+    print(f"Classified image saved to {output_path}")