Object-Based Supervised Land-Cover Classification of High-Resolution Imagery Using ArcGIS Pro Segmentation and scikit-learn Random Forest

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.