Given earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent mapping in hydrology. The problem is uniquely challenging for several reasons: first, the size of earth imagery on a terrain surface is often much larger than the input of popular deep convolutional neural networks; second, there exists topological structure dependency between pixel classes on the surface, and such dependency can follow an unknown and non-linear distribution; third, there are often limited training labels. Existing methods for earth imagery segmentation often divide the imagery into patches and consider the elevation as an additional feature channel. These methods do not fully incorporate the spatial topological structural constraint within and across surface patches and thus often show poor results, especially when training labels are limited. Existing methods on semi-supervised and unsupervised learning for earth imagery often focus on learning representation without explicitly incorporating surface topology. In contrast, we propose a novel framework that explicitly models the topological skeleton of a terrain surface with a contour tree from computational topology, which is guided by the physical constraint (e.g., water flow direction on terrains). Our framework consists of two neural networks: a convolutional neural network (CNN) to learn spatial contextual features on a 2D image grid, and a graph neural network (GNN) to learn the statistical distribution of physics-guided spatial topological dependency on the contour tree. The two models are co-trained via variational EM. Evaluations on the real-world flood mapping datasets show that the proposed models outperform baseline methods in classification accuracy, especially when training labels are limited.
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Deep Variational Instance Segmentation
Instance segmentation, which seeks to obtain both class and instance labels for
each pixel in the input image, is a challenging task in computer vision. State-ofthe-art algorithms often employ a search-based strategy, which first divides the
output image with a regular grid and generate proposals at each grid cell, then
the proposals are classified and boundaries refined. In this paper, we propose
a novel algorithm that directly utilizes a fully convolutional network (FCN) to
predict instance labels. Specifically, we propose a variational relaxation of instance
segmentation as minimizing an optimization functional for a piecewise-constant
segmentation problem, which can be used to train an FCN end-to-end. It extends
the classical Mumford-Shah variational segmentation algorithm to be able to handle
the permutation-invariant ground truth in instance segmentation. Experiments on
PASCAL VOC 2012 and the MSCOCO 2017 dataset show that the proposed
approach efficiently tackles the instance segmentation task. The source code and
trained models are released at https://github.com/jia2lin3yuan1/2020-instanceSeg.
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- NSF-PAR ID:
- 10281811
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- ISSN:
- 1049-5258
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
