SegOptim-A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data

Mucher, C. A. Honrado, Joao P. Goncalves, Joao Pocas, Isabel Marcos, Bruno

SegOptim-A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data

Mucher, C. A. 1Honrado, Joao P. 2Goncalves, Joao 2Pocas, Isabel 3Marcos, Bruno2
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作者信息

  • 1. Wageningen Univ, Earth Informat Subdiv, Wageningen Environm Res Alterra, Droevendaalsesteeg 3, NL-6708 PB Wageningen, Netherlands
  • 2. Univ Porto, Associate Lab, CIBIO Res Ctr Biodivers & Genet Resources, InBIO Res Network Biodivers & Evolutionary Biol, Campus Agr Vairao, P-4485601 Vairao, Portugal
  • 3. Univ Lisbon, Inst Super Agron, Linking Landscape Environm Agr & Food, P-1349017 Lisbon, Portugal
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Abstract

Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods.

Key words

Geographic object-based image analysis/GEOBIA/Image segmentation/Supervised classification/Genetic algorithms/Optimization/High-spatial resolution/Open-source software/R package

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出版年

2019
International journal of applied earth observation and geoinformation

International journal of applied earth observation and geoinformation

SCI
ISSN:0303-2434
被引量14
参考文献量53
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