Object-based Habitat Segmentation
Object-Based Habitat Segmentation is a computational method in remote sensing image analysis that partitions deep-sea habitat data into meaningful spatial units based on spectral, textural, and contextual properties, distinguishing itself from pixel-based approaches by incorporating object-level spatial coherence and scale-dependent segmentation.
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Publications
299
Citations
35
Authors
15
Institutions
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2017–2026
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6
Alan S. Li, Ved Chirayath, Michal Segal‐Rozenhaimer et al. · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2020 · 41 citations · Full text
Habitat-Net: Segmentation of habitat images using deep learning
Jesse F. Abrams, Anand Vashishtha, Seth T. Wong et al. · Ecological Informatics · 2019 · 25 citations
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Ames Research Center
Mountain View, United States
1 h-index
5 publications
205 citations
University of Antwerp
Antwerp, Belgium
1 h-index
3 publications
72 citations
Leibniz Institute for Zoo and Wildlife Research
Berlin, Germany
1 h-index
6 publications
150 citations

University of Wollongong
Wollongong, Australia
1 h-index
1 publications
139 citations
Malaysia University of Science and Technology
Petaling Jaya, Malaysia
1 h-index
1 publications
25 citations
University of New Hampshire at Manchester
Manchester, United States
1 h-index
2 publications
22 citations

The University of Queensland
Brisbane, Australia
1 h-index
7 publications
973 citations
Eomap (Germany)
Seefeld, Germany
1 h-index
1 publications
139 citations
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2 publications · 84 citations
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1 publications · 139 citations
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1 publications · 11 citations
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1 publications · 41 citations
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1 publications · 24 citations
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