Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data
نویسندگان
چکیده
منابع مشابه
Random Forest Algorithm for Land Cover Classification
Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial...
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Zhe Jiang, [email protected] Abstract: My research explores novel computational techniques to map the physical cover (e.g., forests) of the earth’s surface from satellite images. Processing these images is labor-intensive and a significant burden on scientists. Existing methods ignore spatial information and assume that pixels are statistically independent. Consequently, they produce erroneous map...
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ژورنال
عنوان ژورنال: Journal of Big Data
سال: 2020
ISSN: 2196-1115
DOI: 10.1186/s40537-020-00331-8