Question

当尝试在GEE中使用'sampleRectangle（）'函数时遇到问题，它返回1x1数组，我似乎找不到解决方法。请在下面查看我正在使用Justin Braaten发布的方法的python代码。我怀疑传递给该函数的几何对象有问题，但与此同时，我尝试了几种方法来检查此参数的行为方式，并且无法发现任何重大问题。

任何人都可以帮助我了解正在发生的事情吗？

谢谢！

import json
import ee
import numpy as np
import matplotlib.pyplot as plt

ee.Initialize()


point = ee.Geometry.Point([-55.8571, -9.7864])

box_l8sr = ee.Geometry(point.buffer(50).bounds())
box_l8sr2 = ee.Geometry.Polygon(box_l8sr.coordinates())
# print(box_l8sr2)

# Define an image.
# l8sr_y = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_038029_20180810')
oli_sr_coll = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')

## Function to mask out clouds and cloud-shadows present in Landsat images
def maskL8sr(image):
  ## Bits 3 and 5 are cloud shadow and cloud, respectively.
    cloudShadowBitMask = (1 << 3)
    cloudsBitMask = (1 << 5)
  ## Get the pixel QA band.
    qa = image.select('pixel_qa')
  ## Both flags should be set to zero, indicating clear conditions.
    mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
    mask = qa.bitwiseAnd(cloudsBitMask).eq(0)
    return image.updateMask(mask)

l8sr_y = oli_sr_coll.filterDate('2019-01-01', '2019-12-31').map(maskL8sr).mean()

l8sr_bands = l8sr_y.select(['B2', 'B3', 'B4']).sampleRectangle(box_l8sr2)
print(type(l8sr_bands))
# Get individual band arrays.
band_arr_b4 = l8sr_bands.get('B4')
band_arr_b3 = l8sr_bands.get('B3')
band_arr_b2 = l8sr_bands.get('B2')


# Transfer the arrays from server to client and cast as np array.
np_arr_b4 = np.array(band_arr_b4.getInfo())
np_arr_b3 = np.array(band_arr_b3.getInfo())
np_arr_b2 = np.array(band_arr_b2.getInfo())
print(np_arr_b4.shape)
print(np_arr_b3.shape)
print(np_arr_b2.shape)

# Expand the dimensions of the images so they can be concatenated into 3-D.
np_arr_b4 = np.expand_dims(np_arr_b4, 2)
np_arr_b3 = np.expand_dims(np_arr_b3, 2)
np_arr_b2 = np.expand_dims(np_arr_b2, 2)
# # print(np_arr_b4.shape)
# # print(np_arr_b5.shape)
# # print(np_arr_b6.shape)

# # Stack the individual bands to make a 3-D array.
rgb_img = np.concatenate((np_arr_b2, np_arr_b3, np_arr_b4), 2)
# print(rgb_img.shape)

# # Scale the data to [0, 255] to show as an RGB image.
rgb_img_test = (255*((rgb_img - 100)/3500)).astype('uint8')
# plt.imshow(rgb_img)
plt.show()

# # # create L8OLI plot
# fig, ax = plt.subplots()
# ax.set(title = "Satellite Image")
# ax.set_axis_off()
# plt.plot(42, 42, 'ko')
# img = ax.imshow(rgb_img_test, interpolation='nearest')

Answer 1

我也有同样的问题。这似乎与 .mean() 或任何与此相关的图像集合的减少有关。

一种解决方案是在减少后重新投影。例如，您可以尝试在末尾添加“reproject”：

l8sr_y = oli_sr_coll.filterDate('2019-01-01', '2019-12-31').map(maskL8sr).mean().reproject(crs = ee.Projection('EPSG:4326'), scale=30)

它应该可以工作。

gee'sampleRectangle（）'返回1x1数组

1 个答案: