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Merge pull request #45 from Servir-Mekong/development
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Code cleanup and collection updates
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@KMarkert
KMarkert authored Oct 14, 2023
2 parents 355eac7 + 996f245 commit afab7c7
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149 changes: 81 additions & 68 deletions hydrafloods/MODIS_DNNS.py
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Expand Up @@ -5,106 +5,119 @@
import math
import matplotlib.pyplot as pl


def perm_water_mask():
return ee.Image("MODIS/MOD44W/MOD44W_005_2000_02_24").select(['water_mask'], ['b1'])
return ee.Image("MODIS/MOD44W/MOD44W_005_2000_02_24").select(["water_mask"], ["b1"])


def DEM():
return ee.Image('CGIAR/SRTM90_V4')
#ALOS DEM
#ee.Image('JAXA/ALOS/AW3D30_V1_1')
#SRTM DEM
#ee.Image('USGS/SRTMGL1_003')


def GEE_classifier(img, name_classifier, arguments={}):
arguments = {
'training_image' : perm_water_mask(),
'training_band' : "b1",
'training_region' : img
}
c_arguments = {
'subsampling' : 0.1,
'max_classification': 2,
'classifier_mode' : 'classification',
'name_classifier' : name_classifier
}
arguments.update(c_arguments)
classes1 = img.trainClassifier(**arguments)
classes2 = classify(classes1).select(['classification'], ['b1'])
return classes2;
return ee.Image("CGIAR/SRTM90_V4")


# ALOS DEM
# ee.Image('JAXA/ALOS/AW3D30_V1_1')
# SRTM DEM
# ee.Image('USGS/SRTMGL1_003')


# def GEE_classifier(img, name_classifier, arguments={}):
# arguments = {
# "training_image": perm_water_mask(),
# "training_band": "b1",
# "training_region": img,
# }
# c_arguments = {
# "subsampling": 0.1,
# "max_classification": 2,
# "classifier_mode": "classification",
# "name_classifier": name_classifier,
# }
# arguments.update(c_arguments)
# classes1 = img.trainClassifier(**arguments)
# classes2 = classify(classes1).select(["classification"], ["b1"])
# return classes2


def dnns(img):

kernel_scale = 40
VIIRS_ave_scale = 500

k1 = ee.Kernel.square(kernel_scale, 'pixels', False)

composite = img['b1'].addBands(img['b2']).addBands(img['b6'])

classes = GEE_classifier(img, 'Pegasos', {'classifier_mode' : 'probability'})
VIIRS_ave_scale = 500

k1 = ee.Kernel.square(kernel_scale, "pixels", False)

composite = img["b1"].addBands(img["b2"]).addBands(img["b6"])

# classes = GEE_classifier(img, "Pegasos", {"classifier_mode": "probability"})
classes = ee.Image([0.5, 0.5])
water = classes.gte(0.9)
land = classes.lte(0.1)
land = classes.lte(0.1)
mix = water.Not().And(land.Not())
ave_water = water.mask(water).multiply(composite)
ave_water_img = ee.Image([ave_water['b1'], ave_water['b2'], ave_water['b6']])
ave_water_img = ee.Image([ave_water["b1"], ave_water["b2"], ave_water["b6"]])

N_nmin_water = 1
N_water = water.convolve(k1)

water_rf = water.multiply(composite).convolve(k1).multiply(N_water.gte(N_nmin_water)).divide(N_water)

water_rf = (
water.multiply(composite)
.convolve(k1)
.multiply(N_water.gte(N_nmin_water))
.divide(N_water)
)
water_rf = water_rf.add(ave_water_img.multiply(water_rf.Not()))

ave_land = composite.mask(land)
ave_land_img = ee.Image([ave_land['b1'], ave_land['b2'], ave_land['b6']])
# Constraints
R1 = img['b1'].divide(img['b6'])
R2 = img['b2'].divide(img['b6'])
R3 = R1.subtract(water_rf.select('b1').divide(img['b6']) )
R4 = R2.subtract(water_rf.select('b2').divide(img['b6']) )
NR1 = R1.neighborhoodToBands(k1)

ave_land_img = ee.Image([ave_land["b1"], ave_land["b2"], ave_land["b6"]])

# Constraints
R1 = img["b1"].divide(img["b6"])
R2 = img["b2"].divide(img["b6"])
R3 = R1.subtract(water_rf.select("b1").divide(img["b6"]))
R4 = R2.subtract(water_rf.select("b2").divide(img["b6"]))

NR1 = R1.neighborhoodToBands(k1)
NR2 = R2.neighborhoodToBands(k1)
NI1 = img['b1'].neighborhoodToBands(k1)
NI2 = img['b2'].neighborhoodToBands(k1)
NI3 = img['b6'].neighborhoodToBands(k1)


NI1 = img["b1"].neighborhoodToBands(k1)
NI2 = img["b2"].neighborhoodToBands(k1)
NI3 = img["b6"].neighborhoodToBands(k1)

M1 = (NR1.gt(R3)).And(NR1.lt(R1))
M2 = (NR2.gt(R4)).And(NR2.lt(R2))
nLP = M1.And(M2)

NnLP = nLP.reduce(ee.Reducer.sum())
ave_nI1 = NI1.multiply(nLP).reduce(ee.Reducer.sum()).divide(numnLP)
ave_nI2 = NI2.multiply(nLP).reduce(ee.Reducer.sum()).divide(numnLP)
ave_nI1 = NI1.multiply(nLP).reduce(ee.Reducer.sum()).divide(NnLP)
ave_nI2 = NI2.multiply(nLP).reduce(ee.Reducer.sum()).divide(NnLP)
ave_nI3 = NI3.multiply(nLP).reduce(ee.Reducer.sum()).divide(NnLP)

N_nmin_land = 1
ave_land = ave_nI1.addBands(ave_nI2).addBands(ave_nI3)
ave_land = ave_land.multiply(NnLP.gte(N_nmin_land)).add(ave_land_img.multiply(NnLP.lt(N_nmin_land)) )
ave_land = ave_land.multiply(NnLP.gte(N_nmin_land)).add(
ave_land_img.multiply(NnLP.lt(N_nmin_land))
)

ave_landI3 = ave_land.select('b6')
f_water = (ave_landI3.subtract(img['b6'])).divide(ave_landI3.subtract(water_rf.select('b6'))).clamp(0, 1)
ave_landI3 = ave_land.select("b6")
f_water = (
(ave_landI3.subtract(img["b6"]))
.divide(ave_landI3.subtract(water_rf.select("b6")))
.clamp(0, 1)
)
f_water = f_water.add(water).subtract(land).clamp(0, 1)

return f_water


def DEM_downscale(img, f_water):

MODIS_Pixel_meters = 500
DEM_d = img.DEM()

water_present = f_water.gt(0.0)

h_min = DEM_d.mask(f_water).focal_min(MODIS_Pixel_meters, 'square', 'meters')
h_max = DEM_d.mask(f_water).focal_max(MODIS_Pixel_meters, 'square', 'meters')


water_high = h_min.add(h_max.subtract(h_min).multiply(f_water))
water_high = water_high.multiply(f_water.lt(1.0)).multiply(f_water.gt(0.0))
return f_water.eq(1.0).select(['elevation'], ['b1'])
h_min = DEM_d.mask(f_water).focal_min(MODIS_Pixel_meters, "square", "meters")
h_max = DEM_d.mask(f_water).focal_max(MODIS_Pixel_meters, "square", "meters")

water_high = h_min.add(h_max.subtract(h_min).multiply(f_water))
water_high = water_high.multiply(f_water.lt(1.0)).multiply(f_water.gt(0.0))
return f_water.eq(1.0).select(["elevation"], ["b1"])
151 changes: 80 additions & 71 deletions hydrafloods/VIIRS_DNNS.py
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@@ -1,5 +1,4 @@

# coding: utf-8 VIIRS DNNS
# coding: utf-8 VIIRS DNNS

import ee
import numpy as np
Expand All @@ -8,107 +7,117 @@


def perm_water_mask():
return ee.Image("MODIS/MOD44W/MOD44W_005_2000_02_24").select(['water_mask'], ['b1'])
return ee.Image("MODIS/MOD44W/MOD44W_005_2000_02_24").select(["water_mask"], ["b1"])


def DEM():
return ee.Image('CGIAR/SRTM90_V4')
#ALOS DEM
#ee.Image('JAXA/ALOS/AW3D30_V1_1')
#SRTM DEM
#ee.Image('USGS/SRTMGL1_003')


def GEE_classifier(img, name_classifier, arguments={}):
arguments = {
'training_image' : perm_water_mask(),
'training_band' : "b1",
'training_region' : img
}
c_arguments = {
'subsampling' : 0.1,
'max_classification': 2,
'classifier_mode' : 'classification',
'name_classifier' : name_classifier
}
arguments.update(c_arguments)
classes1 = img.trainClassifier(**arguments)
classes2 = classify(classes1).select(['classification'], ['b1'])
return classes2;
return ee.Image("CGIAR/SRTM90_V4")


# ALOS DEM
# ee.Image('JAXA/ALOS/AW3D30_V1_1')
# SRTM DEM
# ee.Image('USGS/SRTMGL1_003')


# def GEE_classifier(img, name_classifier, arguments={}):
# arguments = {
# "training_image": perm_water_mask(),
# "training_band": "b1",
# "training_region": img,
# }
# c_arguments = {
# "subsampling": 0.1,
# "max_classification": 2,
# "classifier_mode": "classification",
# "name_classifier": name_classifier,
# }
# arguments.update(c_arguments)
# classes1 = img.trainClassifier(**arguments)
# classes2 = classify(classes1).select(["classification"], ["b1"])
# return classes2


def dnns(img):

kernel_scale = 40
VIIRS_ave_scale = 500

k1 = ee.Kernel.square(kernel_scale, 'pixels', False)

composite = img['I1'].addBands(img['I2']).addBands(img['I3'])

classes = GEE_classifier(img, 'Pegasos', {'classifier_mode' : 'probability'})
VIIRS_ave_scale = 500

k1 = ee.Kernel.square(kernel_scale, "pixels", False)

composite = img["I1"].addBands(img["I2"]).addBands(img["I3"])

# classes = GEE_classifier(img, "Pegasos", {"classifier_mode": "probability"})
classes = ee.Image([0.5, 0.5])
water = classes.gte(0.9)
land = classes.lte(0.1)
land = classes.lte(0.1)
mix = water.Not().And(land.Not())
ave_water = water.mask(water).multiply(composite)
ave_water_img = ee.Image([ave_water['I1'], ave_water['I2'], ave_water['I3']])
ave_water_img = ee.Image([ave_water["I1"], ave_water["I2"], ave_water["I3"]])

N_nmin_water = 1
N_water = water.convolve(k1)

water_rf = water.multiply(composite).convolve(k1).multiply(N_water.gte(N_nmin_water)).divide(N_water)

water_rf = (
water.multiply(composite)
.convolve(k1)
.multiply(N_water.gte(N_nmin_water))
.divide(N_water)
)
water_rf = water_rf.add(ave_water_img.multiply(water_rf.Not()))

ave_land = composite.mask(land)
ave_land_img = ee.Image([ave_land['I1'], ave_land['I2'], ave_land['I3']])
# Constraints
R1 = img['I1'].divide(img['I3'])
R2 = img['I2'].divide(img['I3'])
R3 = R1.subtract(water_rf.select('I1').divide(img['I3']) )
R4 = R2.subtract(water_rf.select('I2').divide(img['I3']) )
NR1 = R1.neighborhoodToBands(k1)

ave_land_img = ee.Image([ave_land["I1"], ave_land["I2"], ave_land["I3"]])

# Constraints
R1 = img["I1"].divide(img["I3"])
R2 = img["I2"].divide(img["I3"])
R3 = R1.subtract(water_rf.select("I1").divide(img["I3"]))
R4 = R2.subtract(water_rf.select("I2").divide(img["I3"]))

NR1 = R1.neighborhoodToBands(k1)
NR2 = R2.neighborhoodToBands(k1)
NI1 = img['I1'].neighborhoodToBands(k1)
NI2 = img['I2'].neighborhoodToBands(k1)
NI3 = img['I3'].neighborhoodToBands(k1)


NI1 = img["I1"].neighborhoodToBands(k1)
NI2 = img["I2"].neighborhoodToBands(k1)
NI3 = img["I3"].neighborhoodToBands(k1)

M1 = (NR1.gt(R3)).And(NR1.lt(R1))
M2 = (NR2.gt(R4)).And(NR2.lt(R2))
nLP = M1.And(M2)

NnLP = nLP.reduce(ee.Reducer.sum())
ave_nI1 = NI1.multiply(nLP).reduce(ee.Reducer.sum()).divide(numnLP)
ave_nI2 = NI2.multiply(nLP).reduce(ee.Reducer.sum()).divide(numnLP)
ave_nI1 = NI1.multiply(nLP).reduce(ee.Reducer.sum()).divide(NnLP)
ave_nI2 = NI2.multiply(nLP).reduce(ee.Reducer.sum()).divide(NnLP)
ave_nI3 = NI3.multiply(nLP).reduce(ee.Reducer.sum()).divide(NnLP)

N_nmin_land = 1
ave_land = ave_nI1.addBands(ave_nI2).addBands(ave_nI3)
ave_land = ave_land.multiply(NnLP.gte(N_nmin_land)).add(ave_land_img.multiply(NnLP.lt(N_nmin_land)) )

ave_landI3 = ave_land.select('I3')
f_water = (ave_landI3.subtract(img['I3'])).divide(ave_landI3.subtract(water_rf.select('I3'))).clamp(0, 1)
ave_land = ave_land.multiply(NnLP.gte(N_nmin_land)).add(
ave_land_img.multiply(NnLP.lt(N_nmin_land))
)

ave_landI3 = ave_land.select("I3")
f_water = (
(ave_landI3.subtract(img["I3"]))
.divide(ave_landI3.subtract(water_rf.select("I3")))
.clamp(0, 1)
)
f_water = f_water.add(water).subtract(land).clamp(0, 1)

return f_water


def DEM_downscale(img, f_water):

VIIRS_Pixel_meters = 500
DEM_d = img.DEM()

water_present = f_water.gt(0.0)

h_min = DEM_d.mask(f_water).focal_min(VIIRS_Pixel_meters, 'square', 'meters')
h_max = DEM_d.mask(f_water).focal_max(VIIRS_Pixel_meters, 'square', 'meters')


water_high = h_min.add(h_max.subtract(h_min).multiply(f_water))
water_high = water_high.multiply(f_water.lt(1.0)).multiply(f_water.gt(0.0))
return f_water.eq(1.0).select(['elevation'], ['I1'])
h_min = DEM_d.mask(f_water).focal_min(VIIRS_Pixel_meters, "square", "meters")
h_max = DEM_d.mask(f_water).focal_max(VIIRS_Pixel_meters, "square", "meters")

water_high = h_min.add(h_max.subtract(h_min).multiply(f_water))
water_high = water_high.multiply(f_water.lt(1.0)).multiply(f_water.gt(0.0))
return f_water.eq(1.0).select(["elevation"], ["I1"])
4 changes: 3 additions & 1 deletion hydrafloods/__init__.py
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Expand Up @@ -9,6 +9,8 @@
from hydrafloods.floods import *
from hydrafloods import fetch, utils

from hydrafloods import fusion

# from hydrafloods import *

__version__ = "2021.11.10"
__version__ = "2023.10.14"
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