diff --git a/functions/sds.py b/functions/sds.py
index ec62f0e..e9b73fe 100644
--- a/functions/sds.py
+++ b/functions/sds.py
@@ -26,6 +26,7 @@ import skimage.transform as transform
 import sklearn.decomposition as decomposition
 import skimage.measure as measure
 import skimage.morphology as morphology
+from sklearn.cluster import KMeans
 
 
 # import own modules
@@ -605,3 +606,75 @@ def convert_epsg(points, epsg_in, epsg_out):
         raise
         
     return points_converted
+
+def classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool):
+    """
+    Classifies sand pixels using an unsupervised algorithm (Kmeans)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_pan:
+            Panchromatic band
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        wl_pix: list of np.ndarray
+            list of arrays containig the pixel coordinates of the water line
+        buffer_size: int
+            radius of the disk used to create a buffer around the water line
+        min_beach_size: int
+            minimum number of connected pixels belonging to a single beach
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        im_sand: np.ndarray
+            2D binary image containing True where sand pixels are located
+
+    """     
+    # reshape the 2D images into vectors
+    vec_ms_ps = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2])
+    vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1])
+    vec_mask = cloud_mask.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    # add B,G,R,NIR and pan bands to the vector of features
+    vec_features = np.zeros((vec_ms_ps.shape[0], 5))
+    vec_features[:,[0,1,2,3]] = vec_ms_ps[:,[0,1,2,3]]
+    vec_features[:,4] = vec_pan
+    
+    # create binary image with ones where the detected water lines is
+    im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1]))
+    for i, contour in enumerate(wl_pix):
+            indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))]
+            for j, idx in enumerate(indices):
+                im_buffer[idx] = 1
+    # perform a dilation on the binary image
+    se = morphology.disk(buffer_size)
+    im_buffer = morphology.binary_dilation(im_buffer, se)
+    vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    # add cloud mask to buffer
+    vec_buffer= np.logical_and(vec_buffer, ~vec_mask)
+    # perform kmeans (6 clusters)
+    kmeans = KMeans(n_clusters=6, random_state=0).fit(vec_features[vec_buffer,:])
+    labels = np.ones((len(vec_mask))) * np.nan
+    labels[vec_buffer] = kmeans.labels_
+    im_labels = labels.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1])
+    # find the class with maximum reflection in the B,G,R,Pan
+    im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
+    im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+#    im_sand = morphology.binary_dilation(im_sand, morphology.disk(1))
+    
+    if plot_bool:
+        im = np.copy(im_ms_ps)
+        im[im_sand,0] = 0
+        im[im_sand,1] = 0
+        im[im_sand,2] = 1
+        plt.figure()
+        plt.imshow(im[:,:,[2,1,0]])
+        plt.axis('image')
+        plt.title('Sand classification')
+        plt.show()
+    
+    return im_sand
diff --git a/sds_extract.py b/sds_extract.py
index 8de4f40..a6f4126 100644
--- a/sds_extract.py
+++ b/sds_extract.py
@@ -40,6 +40,8 @@ plot_bool = True      # if you want the plots
 prob_high = 99.9        # upper probability to clip and rescale pixel intensity
 min_contour_points = 100# minimum number of points contained in each water line
 output_epsg = 28356     # GDA94 / MGA Zone 56
+buffer_size = 10        # radius of disk for buffer (sand classif parameter)
+min_beach_size = 50     # number of pixels in a beach (sand classif parameter)
 
 # select collection
 satname = 'L8'
@@ -99,3 +101,6 @@ wl_coords = sds.convert_pix2world(wl_pix, crs['crs_15m'])
 
 # convert to output epsg spatial reference
 wl = sds.convert_epsg(wl_coords, crs['epsg_code'], output_epsg)
+
+# classify sand pixels
+im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, im_cloud, wl_pix, buffer_size, min_beach_size, plot_bool)