Intrinsic probability distributions for physical systems

Hung, Tzu-Chao

doi:10.48550/arxiv.1407.8389

Cited by 1 publication

(1 citation statement)

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“…This segmentation method incorporates SAR image intrinsic distribution to guide optimal latent feature variable inference for effective oil spill segmentation. Particularly, in probability theory and statistics, the intrinsic distribution refers to a mathematical function that describes the statistical representation of datapoints, where the physical characteristics for describing data formation are modelled [35]. In the field of machine learning, a latent feature is such feature that is not directly observed but can be extracted through a mathematical model, and it is used to predict the results [36].…”

Section: Construction Of Distribution Guided Efficient Learning For O...mentioning

confidence: 99%

DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation

Chen

Balzter

Zhou

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images, and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.

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Section: Construction Of Distribution Guided Efficient Learning For O...mentioning

confidence: 99%