查找GeoTiff图像中每个像素的纬度/经度坐标

Question

我目前有一个来自GeoTiff文件的171 x 171图像(尽管在其他情况下，我可能有更大的图像(。我的目标是获取图像中的每个像素，并将其转换为纬度/经度对。

我已经能够根据StackOverflow的帖子将图像的角转换为纬度/经度对：从GeoTIFF文件中获取纬度和经度。这篇文章很有帮助，因为我原来的坐标在UTM 15区。

然而，我现在想将图像的所有像素转换为纬度、经度对，并将结果存储在相同维度的numpy数组中。因此，输出将是一个171 x 171 x 2的numpy数组，numpy数组的每个元素都是(经度、纬度(对的元组。

我看到的最相关的帖子是https://scriptndebug.wordpress.com/2014/11/24/latitudelongitude-of-each-pixel-using-python-and-gdal/.然而，这篇文章建议在每个像素上创建一个for循环，并将其转换为纬度和经度。有没有更有效的方法？

为了给我的实际用例提供更多的上下文，我的最终目标是我有一堆卫星图像(例如，在这种情况下，每个图像都是171 x 171(。我正在尝试创建一个建筑分割模型。现在，我正试图通过在每个图像上创建一个掩码来生成标记的数据点，如果像素对应于一栋建筑，则将其标记为1，否则为0。首先，我使用的是Microsoft US Building Footprint数据：https://github.com/microsoft/USBuildingFootprints他们在那里发布了他们检测到的建筑物的多边形(由纬度、经度定义(的GeoJSON文件。我想这样做的方式是：

1. 查找图像中每个像素的纬度和经度。因此，我将获得171 x 171分。将其放入GeoSeries
2. 将点(在GeoSeries中(与Microsoft US Building Footprint数据相交(使用GeoPandas相交：https://geopandas.org/reference.html#geopandas.GeoSeries.intersects)
3. 如果该点与Microsoft US Building Footprint数据中的任何多边形相交，则标记1，否则为0

现在我正在执行步骤(1(，即有效地找到图像中每个像素的纬度/经度坐标。

Answer 1

不幸的是，我还找不到比在所有像素上循环更好的解决方案。到目前为止，我的解决方案是：

import glob
import os
import pickle
import sys
import gdal
import geopandas as gpd
import matplotlib
import matplotlib.pyplot as plt
from numba import jit
import numpy as np
from osgeo import osr
import PIL
from PIL import Image, TiffImagePlugin
from shapely.geometry import Point, Polygon, box
import torch

def pixel2coord(img_path, x, y):
"""
Returns latitude/longitude coordinates from pixel x, y coords
Keyword Args:
img_path: Text, path to tif image
x: Pixel x coordinates. For example, if numpy array, this is the column index
y: Pixel y coordinates. For example, if numpy array, this is the row index
"""
# Open tif file
ds = gdal.Open(img_path)
old_cs = osr.SpatialReference()
old_cs.ImportFromWkt(ds.GetProjectionRef())
# create the new coordinate system
# In this case, we'll use WGS 84
# This is necessary becuase Planet Imagery is default in UTM (Zone 15). So we want to convert to latitude/longitude
wgs84_wkt = """
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.01745329251994328,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]]"""
new_cs = osr.SpatialReference()
new_cs.ImportFromWkt(wgs84_wkt)
# create a transform object to convert between coordinate systems
transform = osr.CoordinateTransformation(old_cs,new_cs) 

gt = ds.GetGeoTransform()
# GDAL affine transform parameters, According to gdal documentation xoff/yoff are image left corner, a/e are pixel wight/height and b/d is rotation and is zero if image is north up. 
xoff, a, b, yoff, d, e = gt
xp = a * x + b * y + xoff
yp = d * x + e * y + yoff
lat_lon = transform.TransformPoint(xp, yp) 
xp = lat_lon[0]
yp = lat_lon[1]

return (xp, yp)

def find_img_coordinates(img_array, image_filename):
img_coordinates = np.zeros((img_array.shape[0], img_array.shape[1], 2)).tolist()
for row in range(0, img_array.shape[0]):
for col in range(0, img_array.shape[1]): 
img_coordinates[row][col] = Point(pixel2coord(img_path=image_filename, x=col, y=row))
return img_coordinates

def find_image_pixel_lat_lon_coord(image_filenames, output_filename):
"""
Find latitude, longitude coordinates for each pixel in the image
Keyword Args:
image_filenames: A list of paths to tif images
output_filename: A string specifying the output filename of a pickle file to store results
Returns image_coordinates_dict whose keys are filenames and values are an array of the same shape as the image with each element being the latitude/longitude coordinates.
"""
image_coordinates_dict = {}
for image_filename in image_filenames:
print('Processing {}'.format(image_filename))
img = Image.open(image_filename)
img_array = np.array(img)
img_coordinates = find_img_coordinates(img_array=img_array, image_filename=image_filename)
image_coordinates_dict[image_filename] = img_coordinates
with open(os.path.join(DATA_DIR, 'interim', output_filename + '.pkl'), 'wb') as f:
pickle.dump(image_coordinates_dict, f)
return image_coordinates_dict

这些是我的助手功能。因为这需要很长时间，所以在find_image_pixel_lat_lon_coord中，我将结果保存到字典image_coordinates_dict中，并将其写入pickle文件以保存结果。

那么我使用这个的方式是：

# Create a list with all tif imagery
image_filenames = glob.glob(os.path.join(image_path_dir, '*.tif'))
image_coordinates_dict = find_image_pixel_lat_lon_coord(image_filenames, output_filename='image_coordinates')