final 2024_Remote_Sensing

2024_Remote_Sensing/kmeans.py

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+import argparse
+import rasterio
+import numpy as np
+from sklearn.cluster import KMeans
+
+def main(input_file, output_file, n_clusters):
+    with rasterio.open(input_file) as src:
+        raster_data = src.read()
+        profile = src.profile
+
+    rows, cols = raster_data.shape[1], raster_data.shape[2]
+    raster_data_reshaped = raster_data.reshape(raster_data.shape[0], -1).T
+
+    kmeans = KMeans(n_clusters=n_clusters, random_state=0)
+    kmeans.fit(raster_data_reshaped)
+    cluster_labels = kmeans.labels_
+
+    classified_raster = cluster_labels.reshape(rows, cols)
+
+    with rasterio.open(
+        output_file, 'w',
+        driver='GTiff',
+        height=rows,
+        width=cols,
+        count=1,
+        dtype=classified_raster.dtype,
+        crs=src.crs,
+        transform=src.transform,
+    ) as dst:
+        dst.write(classified_raster, 1)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Unsupervised classification for raster data.")
+    parser.add_argument("input_file", type=str, help="Path to the input .tif file.")
+    parser.add_argument("output_file", type=str, help="Path to save the classified .tif file.")
+    parser.add_argument("--n_clusters", type=int, default=4, help="Number of clusters for classification (default: 4).")
+    args = parser.parse_args()
+
+    main(args.input_file, args.output_file, args.n_clusters)