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Beyond Finding Change: multitemporal Landsat for forest monitoring and management
Wynne, Randolph H.; Thomas, Valerie A.; Brooks, Evan B.; Coulston, J. O.; Derwin, Jill M.; Liknes, Greg C.; Yang, Z.; Fox, Thomas R.; Ghannam, S.; Abbott, A. Lynn; House, M. N.; Saxena, R.; Watson, Layne T.; Gopalakrishnan, Ranjith (2017-07)
Take homes

Tobler’s Law still in effect with time series – spatial autocorrelation in temporal coherence can help in both preprocessing and estimation

Continual process improvement in extant algorithms needed

Need additional means by which variations within (parameterization) and across algorithms addressed (the Reverend…)

Time series improving higher order products (example with NLCD TCC) enabling near continuous monitoring
Color Invariant Skin Segmentation
Xu, Han; Sarkar, Abhijit; Abbott, A. Lynn (IEEE, 2022-06)
This paper addresses the problem of automatically detecting human skin in images without reliance on color information. A primary motivation of the work has been to achieve results that are consistent across the full range of skin tones, even while using a training dataset that is significantly biased toward lighter skin tones. Previous skin-detection methods have used color cues almost exclusively, and we present a new approach that performs well in the absence of such information. A key aspect of the work is dataset repair through augmentation that is applied strategically during training, with the goal of color invariant feature learning to enhance generalization. We have demonstrated the concept using two architectures, and experimental results show improvements in both precision and recall for most Fitzpatrick skin tones in the benchmark ECU dataset. We further tested the system with the RFW dataset to show that the proposed method performs much more consistently across different ethnicities, thereby reducing the chance of bias based on skin color. To demonstrate the effectiveness of our work, extensive experiments were performed on grayscale images as well as images obtained under unconstrained illumination and with artificial filters. Source code: https://github.com/HanXuMartin/Color-Invariant-Skin-Segmentation
ICESat-2 Early Adopter Summary Results
Abbott, A. Lynn; Wynne, Randolph H.; Thomas, Valerie A. (2019-05-02)
Estimating forest canopy height
PointMotionNet: Point-Wise Motion Learning for Large-Scale LiDAR Point Clouds Sequences
Wang, Jun; Li, Xiaolong; Sullivan, Alan; Abbott, A. Lynn; Chen, Siheng (IEEE, 2022-06)
We propose a point-based spatiotemporal pyramid architecture, called PointMotionNet, to learn motion information from a sequence of large-scale 3D LiDAR point clouds. A core component of PointMotionNet is a novel technique for point-based spatiotemporal convolution, which finds the point correspondences across time by leveraging a time-invariant spatial neighboring space and extracts spatiotemporal features. To validate PointMotionNet, we consider two motion-related tasks: point-based motion prediction and multisweep semantic segmentation. For each task, we design an end-to-end system where PointMotionNet is the core module that learns motion information. We conduct extensive experiments and show that i) for point-based motion prediction, PointMotionNet achieves less than 0.5m mean squared error on Argoverse dataset, which is a significant improvement over existing methods; and ii) for multisweep semantic segmentation, PointMotionNet with a pretrained segmentation backbone outperforms previous SOTA by over 3.3 % mIoU on SemanticKITTI dataset with 25 classes including 6 moving objects.

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Browsing VTechWorks Administration by Author "Abbott, A. Lynn"

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