Automated segmentation parameter selection and classification of urban scenes using open-source software

Object-based image analysis (OBIA) using high spatial resolution remote sensing imagery has been successfully applied in numerous land-use/land-cover studies. OBIA usually requires user inputs at several stages, limiting reproducibility and automation. This work focuses on developing the potential of a relatively simple and general solution to classification of very-high resolution airborne remote sensing imagery. The approach is demonstrated on the datasets made available for the ISPRS 2D Semantic Labeling Challenge and using solely freely available open-source software. In the first step, Large-Scale Mean-Shift Segmentation (LSMS) is used to obtain an initial partitioning of the test images, using the spectral component as the only input to the algorithm. On the output of LSMS, a in-house implementation of Spectral Difference Segmentation (SDS) is performed. Hundreds of candidate segmentation results are produced over a range of scales, by altering input parameters of the segmentation algorithms. A modified version of a well-known unsupervised segmentation evaluation method based on a combination of global inter-segment and intra- segment heterogeneity is employed to objectively rank and select potentially superior segmentation results for subsequent random forest (RF) classification. Statistical features are calculated for each segment based on its pixel values, using spectral (visible and NIR), height (nDSM) and textural (wavelet) data as input. RF classifiers are trained using subsets of the reference data available and their performance is assessed using held-out test data for validation. Results demonstrate the potential of unsupervised segmentation evaluation to reduce user intervention in OBIA and the ability of freely available software to produce high-quality classification results.