Global Remote Sensing Classification

Supervised classification uses labelled training samples to teach the algorithm what each class looks like spectrally; the algorithm then assigns every pixel to one of those predefined classes. Unsupervised classification (e.g., k-means, ISODATA) finds natural spectral clusters without prior labels; the analyst assigns class names afterward. Supervised approaches typically yield higher thematic accuracy when good training data are available.

A common heuristic is at least 10 times the number of spectral bands per class, but for global studies hundreds to thousands of samples per class are advisable to capture intra-class spectral variability across climatic zones. Stratified random sampling by eco-region improves representativeness.

Overall Accuracy can be misleadingly high when one class dominates the landscape because correctly classifying the dominant class inflates the statistic. Kappa adjusts for agreement expected by chance and is more informative for imbalanced class distributions, though it has known limitations and per-class accuracy metrics are increasingly preferred.

OBIA segments the image into spectrally and spatially homogeneous objects before classification, reducing salt-and-pepper noise and exploiting shape and texture. It outperforms global pixel classifiers for high-resolution imagery and complex landscapes where spatial context matters. For coarse- to medium-resolution global mapping, pixel-based classifiers remain common due to computational efficiency.

Only with caution. Sensors differ in spectral band definitions, spatial resolution, and radiometric characteristics, so a model trained on one sensor will underperform on another without cross-calibration or domain adaptation. For multi-temporal studies, consistent pre-processing and — where possible — retraining or fine-tuning on each epoch are recommended.