College of Forest Resources and Environmental Science, Michigan Technological University
Object-based image analysis (OBIA) can improve high-resolution land-cover mapping by grouping pixels into meaningful image objects before classification, thereby reducing the salt-and-pepper noise common in pixel-based methods. This paper presents a reproducible OBIA workflow that integrates mean-shift image segmentation in ArcGIS Pro, rasterization of training polygons and segment identifiers, and supervised machine-learning classification using a Random Forest model implemented in Python (GDAL, NumPy, and scikit-learn). Training labels were transferred to segmented objects using a majority-label rule, while object-level features were computed as mean spectral values from a three-band (RGB) image. The workflow produces a classified raster map with spatially coherent objects, suitable for exploratory land-cover mapping in an urban park. It provides a foundation for adding richer features and formal accuracy assessment in future work.
High-resolution aerial imagery resolves fine-scale landscape elements such as rooftops, sidewalks, and small vegetation patches. Still, pixel-level classification often produces fragmented results because individual pixels vary due to shadows, mixed materials, and sensor noise. Object-based image analysis (OBIA) addresses this limitation by first segmenting imagery into homogeneous objects that better correspond to real-world features, and then classifying those objects using spectral and contextual attributes. This study documents an ArcGIS Pro + Python workflow for OBIA that (i) prepares object and label rasters from segmentation outputs and training polygons and (ii) trains a supervised classifier in scikit-learn to produce an object-consistent land-cover classification map.
2. Data and Study Inputs
The workflow uses four primary inputs: (1) a high-resolution three-band image (PortlandFine.tif), (2) a mean-shift segmentation raster (arcpy_seg_index.tif) representing image objects, (3) training polygons (PortlandTrainingSamples.shp), and (4) rasterized class labels derived from the training polygons (PortlandTrainingSamples_label.tif). Two intermediate rasters are generated to support OBIA sampling: an object identifier raster (sample_id.tif) and a label raster aligned to the reference image.
METHODS
3.1 Preprocessing in ArcGIS Pro
Preprocessing steps were completed in ArcGIS Pro to ensure that the segmentation objects and training labels were rasterized at a consistent cell size, extent, and spatial reference. The key objective was to create (i) an object ID raster for sampling and (ii) a label raster aligned with the imagery for supervised learning.
3.1.1 Task A: Create an object ID raster (sample_id.tif)
The segmentation raster was converted to vector polygons using the Raster To Polygon tool. Segmentation polygons intersecting the training polygons were selected using Select By Location and exported to a new feature class. The exported polygons were then converted back to raster using Polygon To Raster to produce sample_id.tif. Raster environment settings (including cell size and extent) were controlled to match the imagery and to prevent misalignment during sampling.
Moses Tangwam*, Object-Based Supervised Land-Cover Classification of High-Resolution Imagery Using ArcGIS Pro Segmentation and scikit-learn Random Forest, Int. J. Sci. R. Tech., 2026, 3 (1), 287-290. https://doi.org/10.5281/zenodo.18327362
10.5281/zenodo.18327362
