I have a satellite image of 7-channels (Basically I have seven .tif files, one for each band). And I have a .csv file with coordinates of points-of-interest that are in the region shot by the satellite. I want to cut small portions of the image in the surroundings of each coordinate point. How could I do that?
As I don't have a full working code right now, it really doesn't matter the size of those small portions of image. For the explanation of this question let's say that I want them to be 15×15 pixels. So for the moment, my final objective is to obtain a lot of 15x15x7 vectors, one for every coordinate point that I have in the .csv file. And that is what I am stucked with. (the "7" in the "15x15x7" is because the image has 7 channels)
Just to give some background in case it's relevant: I will use those vectors later to train a CNN model in keras.
This is what I did so far: (I am using jupyter notebook, anaconda environment)
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imported gdal, numpy, matplotlib, geopandas, among other libraries.
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Opened the .gif files using gdal, converted them into arrays
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Opened the .csv file using pandas.
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Created a numpy array called "imagen" of shape (7931, 7901, 3) that will host the 7 bands of the satellite image (in form of numbers). At this point I just need to know which rows and colums of the array "imagen" correspond to each coordinate point. In other words I need to convert every coordinate point into a pair of numbers (row,colum). And that is what I am stucked with.
After that, I think that the "cutting part" will be easy.
#I import libraries
from osgeo import gdal_array
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import geopandas
from geopandas import GeoDataFrame
from shapely.geometry import Point
#I access the satellite images (I just show one here to make it short)
b1 = r"E:\Imágenes Satelitales\2017\226_86\1\LC08_L1TP_226086_20170116_20170311_01_T1_sr_band1.tif"
band1 = gdal.Open(b1, gdal.GA_ReadOnly)
#I open the .csv file
file_svc = "C:\\Users\\Administrador\Desktop\DeepLearningInternship\Crop Yield Prediction\Crop Type Classification model - CNN\First\T28_Pringles4.csv"
df = pd.read_csv(file_svc)
print(df.head())
That prints something like this:
Lat1 Long1 CropingState
-37.75737 -61.14537 Barbecho
-37.78152 -61.15872 Verdeo invierno
-37.78248 -61.17755 Barbecho
-37.78018 -61.17357 Campo natural
-37.78850 -61.18501 Campo natural
#I create the array "imagen" (I only show one channel here to make it short)
imagen = (np.zeros(7931*7901*7, dtype = np.float32)).reshape(7931,7901,7)
imagen[:,:,0] = band1.ReadAsArray().astype(np.float32)
#And then I can plot it:
plt.imshow(imagen[:,:,0], cmap = 'hot')
plt.plot()
Which plots something like this:
I want to transform those (-37,-61) into something like (2230,1750). But I haven't figured it how yet. Any clues?
EDIT:
The satellite images are landsat-8 images. According to wikipedia the map projection is UTM. If I run this:
print(gdal.Info(band1))
then I get details of the raster coordinate system:
Driver: GTiff/GeoTIFF
Files: E:\Imágenes Satelitales\2017\226_86\1\LC08_L1TP_226086_20170116_20170311_01_T1_sr_band1.tif
E:\Imágenes Satelitales\2017\226_86\1\LC08_L1TP_226086_20170116_20170311_01_T1_sr_band1.tif.aux.xml
Size is 7901, 7931
Coordinate System is:
PROJCS["WGS 84 / UTM zone 20N",
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0],
UNIT["degree",0.0174532925199433],
AUTHORITY["EPSG","4326"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",-63],
PARAMETER["scale_factor",0.9996],
PARAMETER["false_easting",500000],
PARAMETER["false_northing",0],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
AUTHORITY["EPSG","32620"]]
Origin = (548385.000000000000000,-4030485.000000000000000)
Pixel Size = (30.000000000000000,-30.000000000000000)
Metadata:
AREA_OR_POINT=Area
Band_1=band 1 surface reflectance
Image Structure Metadata:
INTERLEAVE=BAND
Corner Coordinates:
Upper Left ( 548385.000,-4030485.000) ( 62d27'37.04"W, 36d25' 6.01"S)
Lower Left ( 548385.000,-4268415.000) ( 62d26'40.67"W, 38d33'46.31"S)
Upper Right ( 785415.000,-4030485.000) ( 59d49' 6.53"W, 36d22'37.78"S)
Lower Right ( 785415.000,-4268415.000) ( 59d43'34.98"W, 38d31' 6.21"S)
Center ( 666900.000,-4149450.000) ( 61d 6'44.82"W, 37d28'36.79"S)
Band 1 Block=7901x1 Type=Int16, ColorInterp=Gray
Description = band 1 surface reflectance
Min=-1806.000 Max=6547.000
Minimum=-1806.000, Maximum=6547.000, Mean=429.912, StdDev=178.705
NoData Value=-9999
Metadata:
STATISTICS_MAXIMUM=6547
STATISTICS_MEAN=429.9118572176
STATISTICS_MINIMUM=-1806
STATISTICS_STDDEV=178.70509060734
But I don't understand yet how to go on from there.
Best Answer
You can convert x,y to col, row coordinates with
gdal.InvGeoTransform
, andgdal.ApplyGeoTransform
. You can reproject coordinates withosr.CoordinateTransformation.TransformPoint
.E.g.