Hello,
For what I’m doing, I cannot use the SNAP desktop or a graph-based option. I can only use python and the snappy module.
I"m using the snappy module in Python (2.7) and have successfully ran through a tutorial on ocean object (ship) detection. However, I want to export the ship detections as a vector just like the ESA SNAP desktop application does. I’m not seeing any documentation on vector export and the only possible option in File/Export I see is CSV for this purpose. Ideally, a geojson or ESRI ShapeFile would be ideal, but even if CSV is all I can get, that is fine.
Right now, I’ve adapted the code in the ship detection graph tutorial and it references ${outputproduct1} and ${outputproduct2}, with the second one being the GeoTIFF. Then, for the first one (which I am assuming is the vector), I write it as such ProductIO.writeProduct(write_1, write_1_prefix, 'CSV') where write_1 is the GPF.createProduct and write_1_prefix is the file path to save the file. The second one is a GeoTiff, which writes fine.
When I do this, I get an > 30 GB file with no extension for the write_1 output. This surely isn’t a CSV of vector data. I’m not sure what it is.
Any idea on how I can export the ship detections?
One other possible option is to somehow export the ship detection mask raster in the Object-Discrimination folder (titled Sigma0_VH_shp_bit_msk.img). Is that possible?
Update: Code is below:
##############################################################################
import snappy
from snappy import ProductIO
from snappy import HashMap
import os, gc
from snappy import GPF
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = snappy.jpy.get_type('java.util.HashMap')
import time
start = time.time()
path = "C:\\Users\\username\Downloads\\S1A_IW_GRDH_1SDV_20190313T145036_20190313T145101_026321_02F14F_426A\\"
for folder in os.listdir(path):
gc.enable()
print(folder)
output = path + folder + "\\"
timestamp = folder.split("_")[4]
date = timestamp[:8]
#Then, read in the Sentinel-1 data product:
sentinel_1 = ProductIO.readProduct(output + "\\manifest.safe")
print sentinel_1
pols = ['VH','VV']
for p in pols:
polarization = p
# APPLY ORBIT FILE
parameters = HashMap()
parameters.put('orbitType', 'Sentinel Precise (Auto Download)')
parameters.put('polyDegree', '3')
parameters.put('continueOnFail', 'false')
s1_orbit_applied = GPF.createProduct('Apply-Orbit-File', parameters, sentinel_1)
file_prefix=output + date + "_orbit_applied_"+polarization
ProductIO.writeProduct(s1_orbit_applied,file_prefix,'BEAM-DIMAP')
del parameters
# LAND-SEA-MASK
orbit = ProductIO.readProduct(file_prefix + ".dim")
parameters = HashMap()
parameters.put('useSRTM', True)
parameters.put('landMask', True)
parameters.put('shorelineExtension','10')
parameters.put('sourceBands', 'Intensity_' + polarization)
s1_landseamask = result = GPF.createProduct('Land-Sea-Mask', parameters, orbit)
mask_prefix=output + date + "_land-sea-mask_"+polarization
ProductIO.writeProduct(s1_landseamask,mask_prefix,'BEAM-DIMAP')
del parameters
# CALIBRATION
masked = ProductIO.readProduct(mask_prefix + ".dim")
parameters = HashMap()
parameters.put('outputSigmaBand', True)
parameters.put('sourceBands', 'Intensity_' + polarization)
parameters.put('selectedPolarisations', polarization)
#parameters.put('outputImageScaleInDb', True)
calib_applied = output + date + "_calibrate_" + polarization
s1_calibrated = GPF.createProduct("Calibration", parameters, masked)
ProductIO.writeProduct(s1_calibrated, calib_applied, 'BEAM-DIMAP')
del parameters
# ADAPTIVETHRESHOLDING
calibration = ProductIO.readProduct(calib_applied + ".dim")
parameters = HashMap()
parameters.put('targetWindowSizeInMeter','30')
parameters.put('guardWindowSizeInMeter','500.0')
parameters.put('backgroundWindowSizeInMeter','800.0')
parameters.put('pfa','12.5')
adapt_thresh_prefix = output + date + "_adapt-thresh_" + polarization
adapt_thresh = GPF.createProduct("AdaptiveThresholding", parameters, calibration)
ProductIO.writeProduct(adapt_thresh, adapt_thresh_prefix, 'BEAM-DIMAP')
del parameters
# Object-Discrimination
adaptThreshold = ProductIO.readProduct(adapt_thresh_prefix + ".dim")
parameters = HashMap()
parameters.put('minTargetSizeInMeter','30')
parameters.put('maxTargetSizeInMeter','600')
objdisc_prefix = output + date + "_obj-discrimination_" + polarization
objdisc = GPF.createProduct("Object-Discrimination", parameters, adaptThreshold)
ProductIO.writeProduct(objdisc, objdisc_prefix, 'BEAM-DIMAP')
del parameters
# Write 1
obj_disc = ProductIO.readProduct(objdisc_prefix + ".dim")
parameters = HashMap()
parameters.put('file','${outputproduct1}')
#parameters.put('formatName','CSV')
write_1_prefix = output + date + "_prod_1_" + polarization
write_1 = GPF.createProduct("Write", parameters, obj_disc)
ProductIO.writeProduct(write_1, write_1_prefix, 'BEAM-DIMAP')
del parameters
# Write 2
obj_disc = ProductIO.readProduct(objdisc_prefix + ".dim")
parameters = HashMap()
parameters.put('file','${outputproduct2}')
#parameters.put('formatName','Geotiff')
write_2_prefix = output + date + "_prod_2_" + polarization
write_2 = GPF.createProduct("Write", parameters, obj_disc)
ProductIO.writeProduct(write_2, write_2_prefix, 'GeoTIFF')
del parameters`