在AI Earth我用决策树提取出来冬小麦的区域，如何选取样本验证kappa系数和acc系数？
import aie

aie.Authenticate()
aie.Initialize()

feature_collection = aie.FeatureCollection('China_City') \
.filter(aie.Filter.eq('city', '菏泽市'))
region = feature_collection.geometry()

播种期影像

img1 = aie.ImageCollection('SENTINEL_MSIL2A') \
.filterBounds(region) \
.filterDate('2020-10-01', '2020-11-11') \
.filter(aie.Filter.lte('eo:cloud_cover',60)) \
.select(["B11","B8","B4","B3","B2"])\
.median()

         # .map(removeLandsatCloud)          

拔节期影像

img2 = aie.ImageCollection('SENTINEL_MSIL2A')\
.filterDate("2021-02-01", "2021-05-11")\
.filterBounds(region)\
.filter(aie.Filter.lt('eo:cloud_cover', 60))\
.select(["B11","B8","B4","B3"])\
.median()

成熟收获期影像

img3 = aie.ImageCollection('SENTINEL_MSIL2A')\
.filterDate("2021-05-30", "2021-07-5")\
.filterBounds(region)\
.filter(aie.Filter.lt('eo:cloud_cover', 60))\
.select(["B11","B8","B4","B3","B2",])\
.median()
red1 = img1.select("B4")
nir1 = img1.select("B8")
swir1 = img1.select("B11")
red2 = img2.select("B4")
nir2 = img2.select("B8")
red3 = img3.select("B4")
nir3 = img3.select("B8")

ndvi1 = (nir1.subtract(red1)).divide(nir1.add(red1)).rename(["NDVI"]).select("NDVI")
nbr1 = (nir1.subtract(swir1)).divide(nir1.add(swir1)).rename(["NBR"]).select("NBR")
ndvi2 = (nir2.subtract(red2)).divide(nir2.add(red2)).rename(["NDVI"]).select("NDVI")
ndvi3 = (nir3.subtract(red3)).divide(nir3.add(red3)).rename(["NDVI"]).select("NDVI")

小麦在10月份的近红外波段更大，短波红外波段更小

条件1：播种期NDVI小，NBR小

条件2：拔节期抽穗期 NDVI大

条件3：成熟期NDVI小于拔节期NDVI

wheat = ndvi1.lt(aie.Image.constant(0.3))\
.And(nbr1.lt(aie.Image.constant(0.07)))\
.And(ndvi2.gt(aie.Image.constant(0.32)))\
.And(ndvi3.lt(ndvi2))\
.clip(region)

结果可视化

map = aie.Map(
center=region.getCenter(),
height=800,
zoom=7
)

vis_params = {
'color': '#00FF00'
}

map.addLayer(
region,
vis_params,
'region',
bounds=region.getBounds()
)

mask_vis = {
'min': 0,
'max': 1,
'palette': ['#ffffff', '#008000'] # 0:白色, 1:绿色
}

ndvi_vis = {
'min': -0.2,
'max': 0.6,
'palette': ['#d7191c', '#fdae61', '#ffffc0', '#a6d96a', '#1a9641']
}

map.addLayer(ndvi1,ndvi_vis, 'ndvi1', bounds=region.getBounds())
map.addLayer(nir1,ndvi_vis, 'nir1', bounds=region.getBounds())
map.addLayer(ndvi2,ndvi_vis, 'ndvi2', bounds=region.getBounds())
map.addLayer(ndvi3,ndvi_vis, 'ndvi3', bounds=region.getBounds())
map.addLayer(wheat,mask_vis, 'wheat', bounds=region.getBounds()) # 绿色区域为小麦