Extracting the Tailings Ponds from High Spatial Resolution Remote Sensing Images by Integrating a Deep Learning-Based Model

نویسندگان

چکیده

Due to a lack of data and practical models, few studies have extracted tailings pond margins in large areas. In addition, there is no public dataset ponds available for relevant research. This study proposed new deep learning-based framework extracting from high spatial resolution (HSR) remote sensing images by combining You Only Look Once (YOLO) v4 the random forest algorithm. At same time, we created an open source based on HSR images. Taking Tongling city as area, model can detect locations with accuracy efficiency image (precision = 99.6%, recall 89.9%, mean average precision 89.7%). An optimal morphological processing were utilized further extract accurate target The final map entire area was obtained accuracy. Compared algorithm, total extraction time reduced nearly 99%. be beneficial mine monitoring ecological environmental governance.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2021

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs13040743