Generic Geometric Correction¶
These recipes are for casual geospatial imagery users. They use generic math models to improve the geometric accuracy of images so that they better align with other data sources, such as GIS road layers.
For many casual users of geospatial imagery, budgets may not be in place to obtain raw imagery from a vendor that can be processed with specific satellite and or aerial models. Rather, many GIS departments have to use available imagery. As a result, they often obtain imagery with nominal georeferencing, but due to unknown processing procedures, the imagery may not line up well with their GIS data (i.e. road vectors). Since the imagery is already processed, they cannot use the recipes in the Satellite Photogrammetry or Aerial Photogrammetry sections. These recipes can be used to improve the geometric accuracy of images.
Automatic GCP collection and 2D geometric correction with Polynomial Model¶
This recipe provides a quick and automatic method to geometrically correct imagery with georeferencing, but missing a math model or orbital information. This process includes automatic GCP collection and refinement.
This may be useful for improving the georeferencing of archive imagery.
from pci.link import link from pci.autogcp import autogcp from pci.gcprefn import gcprefn from pci.polymodel import polymodel from pci.ortho import ortho #create PIX link file of input tif image for GCP collection input_image = r"C:\generic\miscellaneous_satellite.tif" ingest_link_file = r"C:\generic\miscellaneous_satellite.pix" link(fili=input_image, filo=ingest_link_file) #Automatic GCP collection using initial georeferencing only reference_image = r"C:\generic\reference.tif" num_gcps = [] #AUTOGCP2 returns the number of GCPs that were collected. Can use this for building intelligent scripts gcp_seg = autogcp(mfile=ingest_link_file, dbic=[4], filref=reference_image, dbic_ref=[4], searchr=[25], numgcps=num_gcps) #Refine GCPs based on generic 2D Polynomial model, which is built by the GCPs refined_gcp_seg = [] #GCPREFN returns the segment number with the refined GCPs so that it can be provided into the next func. out_stats = [] #GCPREFN populates this list with 11 useful statistics which can be used to build more intelligence into the GCP refinement process gcprefn(file=ingest_link_file, dbgc=gcp_seg, modeltyp="POLY", reject=[5,1.0,1.0], lasc=refined_gcp_seg, imstat=out_stats) #Compute a 3D generic math model (Thin Plate Spline) from the GCPs you collected and refined polymodel(mfile=ingest_link_file, dbgc=refined_gcp_seg, order=[3]) #Geometrically correct the image with the tps model that was previously computed geo_correct_image = r"C:\Geomatica_Cookbook\generic\corr_random_satellite.tif" res_x = "2" res_y = "2" ortho(mfile=ingest_link_file, filo=geo_correct_image, ftype="TIF")
Automatic GCP collection from road vector layer with generic 3D Geometric Correction¶
For Some GIS organizations it is important that archive imagery lines up well with GIS layers, such as road vector layers. Unfortunately, these images may have been inaccurately corrected and only contain nominal georeferencing.
This recipe demonstrates how to geometrically correct an image with nominal georeferencing using automatic GCP collection and refinement from a vector road layer.
from pci.fftmvec import fftmvec from pci.imageinv import imageinv from pci.fimport import fimport from pci.iii import iii from pci.pcimod import pcimod from pci.gcprefn import gcprefn from pci.tpsmodel import tpsmodel from pci.ortho import ortho input_image = r"C:\generic_road\misc_satellite_image.pix" #Invert the Near-Infrared channel - Helps FFTMVEC to successfully collect GCPs inverse_image = r"C:\generic_road\inv_misc_img.pix" inv_channel = [3] #Near-Infrared channel imageinv(fili=input_image, filo=inverse_image, dbic=inv_channel) #copy the inverted image channel into the ingested image we are going to collect GCPs for pcimod(file=input_image, pciop="ADD", pcival=[0,0,0,1]) iii(fili=inverse_image, filo=input_image, dbic=[1], dboc=[5]) #Run FFTMVEC to automatically collect GCPs from road vectors roadvec = r"C:\generic_road\roads\toronto_vector.pix" gcp_seg = [] fftmvec(file=input_image, dbic=[1,2,4], dbinvc=[5], filv=roadvec, dbvs=[2], vecwidth=[15], lasc=gcp_seg) #Refine GCPs - remove bad GCPs until RMSE falls below 1 pixel in x and y direction refined_gcp_seg = [] gcprefn(file=input_image, dbgc=gcp_seg, modeltyp="POLY", reject=[5,1.0,1.0], lasc=refined_gcp_seg,) #Compute a 3D generic math model (Thin Plate Spline) from the GCPs you collected and refined tpsmodel(mfile=input_image, dbgc=refined_gcp_seg) #Geometrically correct the image with the tps model that was previously computed geo_correct_image = r"C:\generic_road\corr_misc_satellite.pix" dem = r"C:\generic_road\torontodem.pix" ortho(mfile=input_image, filo=geo_correct_image, ftype="TIF", filedem=dem)
Automatic GCP collection and 3D geometric correction with Thin Plate Spline Model¶
This recipe demonstrates how to geometrically correct an image with initial georeferencing using automatic GCP collection and refinement from a georeferenced image and DEM.
from pci.link import link from pci.autogcp import autogcp from pci.gcprefn import gcprefn from pci.tpsmodel import tpsmodel from pci.ortho import ortho #create PIX link file of input tif image for GCP collection input_image = r"C:\generic\miscellaneous_satellite.tif" ingest_link_file = r"C:\generic\miscellaneous_satellite.pix" link(fili=input_image, filo=ingest_link_file) #Automatic GCP collection using initial georeferencing only reference_image = r"C:\generic\reference.tif" dem = r"C:\generic\dem.pix" num_gcps = [] #AUTOGCP2 returns the number of GCPs that were collected. Can use this for building intelligent scripts gcp_seg = autogcp(mfile=ingest_link_file, dbic=[4], filref=reference_image, dbic_ref=[4], filedem=dem, searchr=[25], numgcps=num_gcps) #Refine GCPs based on generic 2D Polynomial model, which is built by the GCPs refined_gcp_seg = [] #GCPREFN returns the segment number with the refined GCPs so that it can be provided into the next func. out_stats = [] #GCPREFN populates this list with 11 useful statistics which can be used to build more intelligence into the GCP refinement process gcprefn(file=ingest_link_file, dbgc=gcp_seg, modeltyp="POLY", reject=[5,1.0,1.0], lasc=refined_gcp_seg, imstat=out_stats) #Compute a 3D generic math model (Thin Plate Spline) from the GCPs you collected and refined tpsmodel(mfile=ingest_link_file, dbgc=refined_gcp_seg) #Geometrically correct the image with the tps model that was previously computed geo_correct_image = r"C:\generic\corr_random_satellite.tif" ortho(mfile=ingest_link_file, filo=geo_correct_image, ftype="TIF", filedem=dem)
Automatic GCP collection and 3D geometric correction with Thin Plate Spline Model - Batch Processing¶
This recipe demonstrates how to automatically discover all valid images in a directory, automatically collect GCPs, refine the GCPs based on RMSE and finally geometrically correct all valid images found using a Thin Plate Spline model (TPS).
import sys, fnmatch, os from pci.link import link from pci.autogcp import autogcp from pci.gcprefn import gcprefn from pci.tpsmodel import tpsmodel from pci.ortho import ortho raw_dir = r"D:\generic_batch\raw" file_filter = "*.tif" raw_image_list = [] for r,d,f in os.walk(raw_dir): for file in fnmatch.filter(f, file_filter): print "found valid input file: ", file raw_image_list.append(os.path.join(r,file)) if len(raw_image_list) > 0: for raw_image in raw_image_list: #Create .pix link file to store the GCP segement that holds the GCPs ingest_link_dir = r"D:\generic_batch\ingest" ingest_link_filename = os.path.splitext(os.path.basename(raw_image))[0] + ".pix" ingest_link_image = os.path.join(ingest_link_dir, ingest_link_filename) link(fili=raw_image, filo=ingest_link_image) #Automatic GCP collection using initial georeferencing only reference_image = r"D:\PCI_Work\Projects\Open\Marketing\Python_Cookbook\tests\generic_batch\reference\o2_5m.pix" dem = r"D:\PCI_Work\Projects\Open\Marketing\Python_Cookbook\tests\generic_batch\reference\torontodem.pix" num_gcps = [] #AUTOGCP2 returns the number of GCPs that were collected. Can use this for building intelligent scripts gcp_seg = autogcp(mfile=ingest_link_image, dbic=[2], filref=reference_image, dbic_ref=[2], filedem=dem, searchr=[50], numgcps=num_gcps) #Refine GCPs based on generic 2D Polynomial model, which is built by the GCPs gcprefn(file=ingest_link_image, dbgc=refined_gcp_seg, modeltyp="POLY", reject=[5, 0.5, 0.5], lasc=refined_gcp_seg, imstat=out_stats) #Compute a 3D generic math model (Thin Plate Spline) from the GCPs you collected and refined tpsmodel(mfile=ingest_link_image, dbgc=refined_gcp_seg) else: print "No image files found in " + raw_dir + " that match your filter criteria." sys.exit() #Geometrically correct the images with the tps model geo_correct_dir = r"D:\PCI_Work\Projects\Open\Marketing\Python_Cookbook\tests\generic_batch\corrected" ortho(mfile=ingest_link_dir, filo=geo_correct_dir, ftype="TIF", filedem=dem)
