Critical information about surface water bodies, particularly their dynamic behavior, is most effectively derived from water contour detection. However, the accurate detection of contours is complicated by the land–water ambiguity and the great imbalance between contour and non-contour data. A unique fully convolutional multiscale UNet-styled (MS UNet) deep network is proposed for accurate water contour detection in the visible spectrum. The MS UNet utilizes blocks of multiscale convolutional filters to improve contour detection and employs loss functions to correct the imbalance between contour and non-contour data, as well as capture the loss at both the pixel and object levels. The proposed system is shown to be more effective at detecting water contours than recent water detection systems and other popular image segmentation networks while using a fraction of the parameters.
In this article, we explore the role and usefulness of parts-based spatial concept learning about complex scenes. Specifically, we consider the process of teaching a spatially attributed graph how to utilize parts-detectors and relative positions as attributes in order to learn concepts and to produce human oriented explanations. First, we endow the graph with parts detectors and relative positions to determine the possible range of attributes that will limit the types of concepts that are learned. Next, we enable the graph to learn concepts in the context of recognizing structured objects in imagery and the spatial relations between these objects. As the graph is learning concepts, we allow human operators to give feedback on attribute knowledge, creating a system that can augment expert knowledge for any similar task. Effectively, we show how to perform online concept learning of a spatially attributed graph. This route was chosen due to the vast representational capabilities of attributed graphs, and the low-data requirement of online learning. Finally, we explore how well this method lends itself to human augmentation, leveraging human expertise to perform otherwise difficult tasks for a machine. Our experiments shed light on the usefulness of spatially attributed graphs utilizing online concept learning, and shows the way forward for more explainable image reasoning machines.
Conference Committee Involvement (3)
Geospatial Informatics XIII
4 May 2023 | Orlando, Florida, United States
Geospatial Informatics XII
6 April 2022 | Orlando, Florida, United States
Geospatial Informatics XI
12 April 2021 | Online Only, Florida, United States
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