Refined UNet: UNet-Based Refinement Network for Cloud and Shadow Precise Segmentation

Formulated as a pixel-level labeling task, data-driven neural segmentation models for cloud and corresponding shadow detection have achieved a promising accomplishment in remote sensing imagery processing. The limited capability of these methods to delineate the boundaries of clouds and shadows, how...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-06, Vol.12 (12), p.2001
Main Authors: Jiao, Libin, Huo, Lianzhi, Hu, Changmiao, Tang, Ping
Format: Article
Language:English
Subjects:
adaptive weights
Boundaries
Classification
cloud and shadow segmentation
Clouds
Conditional random fields
Datasets
Experiments
fully-connected conditional random field
Green, Red
Image detection
Image segmentation
Landsat
Landsat satellites
pixel-level labelling
Proposals
Remote sensing
Satellite imagery
Semantics
Shadows
Statistical methods
UNet prediction
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Summary:Formulated as a pixel-level labeling task, data-driven neural segmentation models for cloud and corresponding shadow detection have achieved a promising accomplishment in remote sensing imagery processing. The limited capability of these methods to delineate the boundaries of clouds and shadows, however, is still referred to as a central issue of precise cloud and shadow detection. In this paper, we focus on the issue of rough cloud and shadow location and fine-grained boundary refinement of clouds on the dataset of Landsat8 OLI and therefore propose the Refined UNet to achieve this goal. To this end, a data-driven UNet-based coarse prediction and a fully-connected conditional random field (Dense CRF) are concatenated to achieve precise detection. Specifically, the UNet network with adaptive weights of balancing categories is trained from scratch, which can locate the clouds and cloud shadows roughly, while correspondingly the Dense CRF is employed to refine the cloud boundaries. Eventually, Refined UNet can give cloud and shadow proposals sharper and more precisely. The experiments and results illustrate that our model can propose sharper and more precise cloud and shadow segmentation proposals than the ground truths do. Additionally, evaluations on the Landsat 8 OLI imagery dataset of Blue, Green, Red, and NIR bands illustrate that our model can be applied to feasibly segment clouds and shadows on the four-band imagery data.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12122001