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Optimization of urban land-cover classification workflow based on geographic-object analysis using very-high-resolution imagery
Optimización de un flujo de trabajo para clasificación de coberturas urbanas basado en análisis de objetos geográficos usando imágenes de alta resolución
DOI:
https://doi.org/10.15446/dyna.v89n220.98902Palabras clave:
Geographic-Object Based Image Analysis, Multiresolution Segmentation, Random Forest, Urban Land-Cover Classification, Optimization (en)Análisis orientado a objetos, segmentación multirresolución, bosques aleatorios, clasificación de coberturas urbanas, optimización (es)
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A recurring problem in Geographic-Object Based Image Analysis (GEOBIA) is the need to tune each one of the three phases involved in the process, segmentation, feature selection, and classification. This paper presents the optimization of a GEOBIA-based urban land-cover classification workflow using very-high-resolution imagery. Two classification workflows are evaluated: (i) a non-optimized workflow; and (ii) an optimized workflow. In the segmentation and classification phases, both workflows used the multi-resolution segmentation algorithm and the random forest classification algorithm. In addition, important spectral, geomorphometric, and textural features are identified as significant predictor variables for the final classification. It is shown that the classification accuracy of every land-cover category increases with optimization, resulting in an overall accuracy increase of 9.34% compared with no optimization. The results show the substantial impact that optimization has on final classification output and suggest the importance of its adoption as a good practice in GEOBIA-based land-cover classification.
Un problema recurrente en el análisis de imágenes orientado a objetos geográficos (GEOBIA), es la necesidad de ajustar las tres fases involucradas en el proceso, es decir, segmentación, selección de atributos y clasificación. Este artículo presenta la optimización del flujo de trabajo para clasificación de coberturas urbanas basado en GEOBIA empleando imágenes de alta resolución. Se evalúan dos flujos de trabajo para clasificación: (i) no optimizado; y (ii) optimizado. Ambos utilizaron los algoritmos multi-resolution segmentation y random forest en las fases de segmentación y clasificación respectivamente. Además, se identifican características espectrales, geomorfométricas y texturales, como variables predictoras significativas para la clasificación final. Se muestra que la exactitud de la clasificación en cada cobertura aumenta con la optimización, lo que resulta en un aumento de la exactitud global de 9.34% respecto a la clasificación no optimizada. Los resultados muestran el impacto que tiene la optimización en el resultado final de la clasificación y sugieren la importancia de adoptarla como buena práctica en la clasificación de coberturas basada en GEOBIA.
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1. D.V. Uchaev, R.R. Zlonikov, Dm.V. Uchaev. (2026). Developing a unified framework for describing classes of land cover components in object-based classification of aerospace imagery. Geodesy and Cartography, 1026(12), p.46. https://doi.org/10.22389/0016-7126-2025-1026-12-46-55.
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