forked from kilianv/CoastSat_WRL
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
176 lines
6.8 KiB
Python
176 lines
6.8 KiB
Python
# -*- coding: utf-8 -*-
|
|
|
|
#==========================================================#
|
|
# Run Neural Network on image to extract sandy pixels
|
|
#==========================================================#
|
|
|
|
# Initial settings
|
|
import os
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import ee
|
|
import pdb
|
|
import time
|
|
import pandas as pd
|
|
# other modules
|
|
from osgeo import gdal, ogr, osr
|
|
import pickle
|
|
import matplotlib.cm as cm
|
|
from pylab import ginput
|
|
|
|
# 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.measure as measure
|
|
import skimage.morphology as morphology
|
|
from scipy import ndimage
|
|
|
|
# machine learning modules
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.neural_network import MLPClassifier
|
|
from sklearn.preprocessing import StandardScaler, Normalizer
|
|
from sklearn.externals import joblib
|
|
|
|
# import own 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'] = True
|
|
plt.rcParams['figure.max_open_warning'] = 100
|
|
ee.Initialize()
|
|
|
|
# parameters
|
|
cloud_thresh = 0.5 # threshold for cloud cover
|
|
plot_bool = False # if you want the plots
|
|
prob_high = 100 # 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)
|
|
|
|
# load metadata (timestamps and epsg code) for the collection
|
|
satname = 'L8'
|
|
sitename = 'NARRA_all'
|
|
#sitename = 'NARRA'
|
|
#sitename = 'OLDBAR'
|
|
|
|
# Load metadata
|
|
filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
|
|
|
|
# path to images
|
|
file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
|
|
file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
|
|
file_names_pan = os.listdir(file_path_pan)
|
|
file_names_ms = os.listdir(file_path_ms)
|
|
N = len(file_names_pan)
|
|
|
|
# initialise some variables
|
|
idx_skipped = []
|
|
idx_nocloud = []
|
|
n_features = 10
|
|
train_pos = np.nan*np.ones((1,n_features))
|
|
train_neg = np.nan*np.ones((1,n_features))
|
|
columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'SAND')
|
|
|
|
clf = joblib.load(os.path.join(os.getcwd(), 'sand_classification', 'NN_new.pkl'))
|
|
#%%
|
|
for i in range(N):
|
|
# read pan image
|
|
fn_pan = os.path.join(file_path_pan, file_names_pan[i])
|
|
data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
|
|
georef = np.array(data.GetGeoTransform())
|
|
bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
|
|
im_pan = np.stack(bands, 2)[:,:,0]
|
|
# read ms image
|
|
fn_ms = os.path.join(file_path_ms, file_names_ms[i])
|
|
data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
|
|
bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
|
|
im_ms = np.stack(bands, 2)
|
|
# cloud mask
|
|
im_qa = im_ms[:,:,5]
|
|
cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
|
|
cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
|
|
order=0, preserve_range=True,
|
|
mode='constant').astype('bool_')
|
|
# resize the image using bilinear interpolation (order 1)
|
|
im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
|
|
order=1, preserve_range=True, mode='constant')
|
|
# check if -inf or nan values and add to cloud mask
|
|
im_inf = np.isin(im_ms[:,:,0], -np.inf)
|
|
im_nan = np.isnan(im_ms[:,:,0])
|
|
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
|
|
# skip if cloud cover is more than the threshold
|
|
cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
|
|
if cloud_cover > cloud_thresh:
|
|
print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
|
|
idx_skipped.append(i)
|
|
continue
|
|
idx_nocloud.append(i)
|
|
|
|
# rescale intensities
|
|
im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool)
|
|
im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool)
|
|
# pansharpen rgb image
|
|
im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
|
|
nrow = im_ms_ps.shape[0]
|
|
ncol = im_ms_ps.shape[1]
|
|
# 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], cloud_mask, plot_bool)
|
|
# detect edges
|
|
wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
|
|
# classify sand pixels with Kmeans
|
|
im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool)
|
|
|
|
# calculate features
|
|
im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features))
|
|
im_features[:,:,[0,1,2,3,4]] = im_ms_ps
|
|
im_features[:,:,5] = im_pan
|
|
im_features[:,:,6] = im_ndwi
|
|
im_features[:,:,7] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask, False) # ND(NIR-R)
|
|
im_features[:,:,8] = sds.nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask, False) # ND(B-R)
|
|
im_features[:,:,9] = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # (SWIR-G)
|
|
# remove NaNs and clouds
|
|
vec = im_features.reshape((im_ms_ps.shape[0] * im_ms_ps.shape[1], n_features))
|
|
vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1])
|
|
vec_nan = np.any(np.isnan(vec), axis=1)
|
|
vec_mask = np.logical_or(vec_cloud, vec_nan)
|
|
vec = vec[~vec_mask, :]
|
|
# predict with NN
|
|
y = clf.predict(vec)
|
|
# recompose image
|
|
vec_new = np.zeros((cloud_mask.shape[0]*cloud_mask.shape[1]))
|
|
vec_new[~vec_mask] = y
|
|
im_classif = vec_new.reshape((im_ms_ps.shape[0], im_ms_ps.shape[1]))
|
|
im_classif = im_classif.astype(bool)
|
|
im_classif = morphology.remove_small_objects(im_classif, min_size=min_beach_size, connectivity=2)
|
|
|
|
# make comparison plot between NN and Kmeans
|
|
im1 = np.copy(im_ms_ps)
|
|
im1[im_classif,0] = 0
|
|
im1[im_classif,1] = 0
|
|
im1[im_classif,2] = 1
|
|
|
|
im2 = np.copy(im_ms_ps)
|
|
im2[im_sand,0] = 0
|
|
im2[im_sand,1] = 0
|
|
im2[im_sand,2] = 1
|
|
|
|
plt.figure()
|
|
ax1 = plt.subplot(121)
|
|
plt.imshow(im1[:,:,[2,1,0]])
|
|
plt.axis('off')
|
|
plt.title('NN')
|
|
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
|
|
plt.imshow(im2[:,:,[2,1,0]])
|
|
plt.axis('off')
|
|
plt.title('Kmeans')
|
|
mng = plt.get_current_fig_manager()
|
|
mng.window.showMaximized()
|
|
plt.tight_layout()
|
|
plt.draw() |