Class
Article
College
College of Science
Department
Mathematics and Statistics Department
Faculty Mentor
Kevin Moon
Presentation Type
Oral Presentation
Abstract
Compared to color images, hyperspectral images are high dimensional, containing hundreds of channels of information. To distill this information, and capture spatial information, image segmentation is used to group similar pixels together. A popular image segmentation algorithm is the marker-based Watershed Transform. One difficulty with this algorithm is choosing the markers, or locations, that seed the algorithm. There are various approaches for automatic marker placement depending on the application, with little consensus on the most general method for hyperspectral images. We propose using an ensemble of random segmentations. Specifically, we investigate a simple, unbiased random marker placement strategy to generate an ensemble of diverse segmentations. Our methods were tested on four standard hyperspectral images, Indian Pines and Salinas (rural), and Pavia University and Center (urban), looking at overall accuracy to compare performance. We find that the ensemble methods perform equivalently or better than single segmentation and is more stable than generating a single segmentation.
Location
Logan, UT
Start Date
4-11-2023 10:30 AM
End Date
4-11-2023 11:30 AM
Introduction to Ensemble Watershed Segmentation
Logan, UT
Compared to color images, hyperspectral images are high dimensional, containing hundreds of channels of information. To distill this information, and capture spatial information, image segmentation is used to group similar pixels together. A popular image segmentation algorithm is the marker-based Watershed Transform. One difficulty with this algorithm is choosing the markers, or locations, that seed the algorithm. There are various approaches for automatic marker placement depending on the application, with little consensus on the most general method for hyperspectral images. We propose using an ensemble of random segmentations. Specifically, we investigate a simple, unbiased random marker placement strategy to generate an ensemble of diverse segmentations. Our methods were tested on four standard hyperspectral images, Indian Pines and Salinas (rural), and Pavia University and Center (urban), looking at overall accuracy to compare performance. We find that the ensemble methods perform equivalently or better than single segmentation and is more stable than generating a single segmentation.