From c31ae42fbd05b9735259b727e6c6a62479061631 Mon Sep 17 00:00:00 2001
From: kvos <voskilian@gmail.com>
Date: Mon, 9 Apr 2018 16:34:54 +1000
Subject: [PATCH] implemented sand classification

---
 classify_sand.py | 181 +++++++++++++++++++++++++++++++++++++++++++++++
 functions/sds.py |  73 +++++++++++++++++++
 2 files changed, 254 insertions(+)
 create mode 100644 classify_sand.py

diff --git a/classify_sand.py b/classify_sand.py
new file mode 100644
index 0000000..b5349be
--- /dev/null
+++ b/classify_sand.py
@@ -0,0 +1,181 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Extract shorelines from Landsat images
+#==========================================================#
+
+# Initial settings
+import ee
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import pickle
+import pdb
+import pytz
+
+
+# image processing modules
+import skimage.filters as filters 
+import skimage.exposure as exposure
+import skimage.transform as transform
+import sklearn.decomposition as decomposition
+import skimage.morphology as morphology
+import skimage.measure as measure
+
+# machine learning modules
+from sklearn.cluster import KMeans
+
+# my modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = False
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.5      # threshold for cloud cover
+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 (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 50     # number of pixels in a beach (pixel classification)
+
+# select collection
+satname = 'L8'
+input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') # Landsat 8 Tier 1 TOA
+
+# location (Narrabeen-Collaroy beach)
+polygon = [[[151.3473129272461,-33.69035274454718],
+            [151.2820816040039,-33.68206818063878], 
+            [151.27281188964844,-33.74775138989556],
+            [151.3425064086914,-33.75231878701767],
+            [151.3473129272461,-33.69035274454718]]];
+
+# dates
+start_date = '2013-01-01'
+end_date = '2018-12-31'
+
+# filter by location and date
+flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date)
+
+n_img = flt_col.size().getInfo()
+print('Number of images covering the polygon:', n_img)
+im_all = flt_col.getInfo().get('features')
+
+i = 0 # first image
+    
+# find image in ee database
+im = ee.Image(im_all[i].get('id')) 
+
+# load image as np.array
+im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, polygon, satname, plot_bool)
+
+# rescale intensities
+im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
+im_pan = sds.rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
+
+# pansharpen rgb image
+im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, plot_bool)
+
+# add down-sized bands for NIR and SWIR (since pansharpening is not possible)
+im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2) 
+
+# calculate NDWI
+im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud, plot_bool)
+
+# edge detection
+wl_pix = sds.find_wl_contours(im_ndwi, im_cloud, min_contour_points, plot_bool)
+
+# convert from pixels to world coordinates
+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)
+
+#%%
+plt.figure()
+plt.imshow(im_ms_ps[:,:,[2,1,0]])
+for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
+plt.axis('image')
+plt.title('Detected water lines')
+plt.show()
+
+vec = 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])
+features = np.zeros((len(vec), 5))
+features[:,[0,1,2,3]] = vec[:,[0,1,2,3]]
+features[:,4] = vec_pan
+vec_mask = im_cloud.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+
+# create buffer
+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
+        
+        
+plt.figure()
+plt.imshow(im_buffer)
+plt.draw()
+
+se = morphology.disk(buffer_size)
+im_buffer = morphology.binary_dilation(im_buffer, se)
+
+plt.figure()
+plt.imshow(im_buffer)
+plt.draw()
+
+vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+
+vec_buffer= np.logical_and(vec_buffer, ~vec_mask)
+
+#vec_buffer = np.ravel_multi_index(z,(im_ms_ps.shape[0], im_ms_ps.shape[1]))
+
+
+kmeans = KMeans(n_clusters=6, random_state=0).fit(vec[vec_buffer,:])
+labels = kmeans.labels_
+labels_full = np.ones((len(vec_mask))) * np.nan
+labels_full[vec_buffer] = labels
+im_labels = labels_full.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1])
+
+plt.figure()
+plt.imshow(im_labels)
+plt.axis('equal')
+plt.draw()
+
+utils.compare_images(im_labels, im_pan)
+
+plt.figure()
+for i in range(6): plt.plot(kmeans.cluster_centers_[i,:])
+plt.draw()
+
+im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
+
+im_sand2 = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+im_sand3 = morphology.binary_dilation(im_sand2, morphology.disk(1))
+plt.figure()
+plt.imshow(im_sand3)
+plt.draw()
+
+im_ms_ps[im_sand3,0] = 0
+im_ms_ps[im_sand3,1] = 0
+im_ms_ps[im_sand3,2] = 1
+
+
+plt.figure()
+plt.imshow(im_ms_ps[:,:,[2,1,0]])
+plt.axis('image')
+plt.title('Sand classification')
+plt.show()
+
+#%%
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