To for application to very large remote sensing datasets, an approach to “Scalable image segmentation” presented in [1] using RSGISLib. In the paper a 30 m spatial resolution satellite mosaic of Australia was segmented by splitting into tiles, processing each tile on a separate node of a HPC, merging and then performing a second segmentation to remove artefacts at tile boundaries.
For a full description of the approach see section 5.3 of the open access paper (available to view online at http://www.mdpi.com/2072-4292/6/7/6111/htm).
A version of this approach, designed to be used on a single machine, is now available in RSGISLib through the performTiledSegmentation function. The function is called in a similar way to the existing runShepherdSegmentation function.
For instructions on installing the latest version of RSGISLib see this post, skipping to section 7 if you already have Linux or OS X installed.
The code below presents an example of applying either the tiled or standard segmentation through the use of a command line flag:
#!/usr/bin/env python
"""
Apply segmentation to image
"""
from __future__ import print_function
import argparse
import shutil
import sys
import tempfile
from rsgislib.segmentation import tiledsegsingle
from rsgislib.segmentation import segutils
# Set values for clustering
NUM_CLUSTERS = 60
MIN_PIXELS = 100
DIST_THRESHOLD = 100
# Set values for tiles segmentation
TILE_WIDTH = 2000
TILE_HEIGHT = 2000
parser = argparse.ArgumentParser()
parser.add_argument("inputimage", nargs=1,
type=str, help="Input Image")
parser.add_argument("outputclumps", nargs=1,
type=str, help="Output clumps")
parser.add_argument("--tiled",
default=False,
action="store_true",
help="Use tiled segmentation")
args = parser.parse_args()
# Make temp directory for intermediate files
temp_dir = tempfile.mkdtemp(prefix='rsgislib_seg_')
if args.tiled:
# If requested run tiled segmentation
tiledsegsingle.performTiledSegmentation(args.inputimage[0],
args.outputclumps[0],
tmpDIR=temp_dir,
tileWidth=TILE_WIDTH,
tileHeight=TILE_HEIGHT,
validDataThreshold=0.3,
numClusters=NUM_CLUSTERS,
minPxls=MIN_PIXELS,
distThres=DIST_THRESHOLD,
sampling=100, kmMaxIter=200)
else:
# If not run standard
segutils.runShepherdSegmentation(args.inputimage[0],
args.outputclumps[0],
tmpath=temp_dir,
numClusters=NUM_CLUSTERS,
minPxls=MIN_PIXELS,
distThres=DIST_THRESHOLD,
sampling=100, kmMaxIter=200)
shutil.rmtree(temp_dir)
Note, script edited for post – full version available from GitHub.
[1] Clewley, D.; Bunting, P.; Shepherd, J.; Gillingham, S.; Flood, N.; Dymond, J.; Lucas, R.; Armston, J.; Moghaddam, M. A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables. Remote Sensing 2014, 6, 6111-6135. http://www.mdpi.com/2072-4292/6/7/6111
spectraldifferences 