Dam Reservoir Extraction From Remote Sensing Imagery Using Tailored Metric Learning Strategies
نویسندگان
چکیده
Dam reservoirs play an important role in meeting sustainable development goals (SDGs) and global climate targets. However, particularly for small dam reservoirs, there is a lack of consistent data on their geographical location. To address this gap, promising approach to perform automated reservoir extraction based globally available remote sensing imagery. It can be considered as fine-grained task water body extraction, which involves extracting areas images then separating from natural bodies. A straightforward solution extend the commonly used binary-class segmentation multiclass. This, however, does not work well exists much pixel-level difference between We propose novel deep neural network (DNN)-based pipeline that decomposes into recognition. Water bodies are first separated background lands model each individual predicted either or classification model. For former step, point-level metric learning (PLML) with triplets across injected contour ambiguities land regions. latter prior-guided (PGML) clusters optimize image embedding space level clusters. facilitate future research, we establish benchmark dataset Earth imagery human-labeled river basins West Africa India. Extensive experiments were conducted task, recognition joint task. Superior performance has been observed respective tasks when comparing our method state-of-the-art approaches. The codes datasets at https://github.com/c8241998/Dam-Reservoir-Extraction .
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ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2022
ISSN: ['0196-2892', '1558-0644']
DOI: https://doi.org/10.1109/tgrs.2022.3172883