文献类型： 外文期刊
第一作者： Feng, Tianjing
作者： Feng, Tianjing;Cheng, Xinwen;Ma, Hairong
作者机构：
关键词： Random Forest; maximum voting entropy; generalized Euclidean distance; high-resolution remote sensing imagery; greenhouse identification
期刊名称：IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM
ISSN： 2153-6996
年卷期： 2020 年
页码：
收录情况： SCI
摘要： The timely and accurate acquisition of greenhouses and their distribution from remote sensing imagery is valuable for Chinese authorities seeking to optimize regional agricultural management and mitigate environmental pollution. However, greenhouses are uncommon background objects in such imagery, making them a minority class that traditional random forest (RF) methods struggle to classify accurately in unbalanced data sets. Herein, we propose and test an improved RF sample selection method. Equal sample numbers were randomly selected from minority and majority classes to build an original training set for RF modeling. High-quality samples were then automatically added to the training set according to the voting entropy and generalized Euclidean distance, which are based on sample characteristic parameters. The results demonstrate that our improved RF yields better results in identifying greenhouses than the traditional RF. In addition, our method can be utilized to identify other minority-class objects from remote sensing imagery.
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