کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
555862 874165 2016 8 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Random forest in remote sensing: A review of applications and future directions
ترجمه فارسی عنوان
جنگل های تصادفی در سنجش از راه دور: مروری بر برنامه های کاربردی و دستورالعمل های آینده
کلمات کلیدی
جنگل های تصادفی؛ طبقه بندی نظارت شده. طبقه بندی آنسامبل؛ مرور؛ انتخاب ویژگی
Random forest; Supervised classifier; Ensemble classifier; Review; Feature selection
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی

A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables. This classifier has become popular within the remote sensing community due to the accuracy of its classifications. The overall objective of this work was to review the utilization of RF classifier in remote sensing. This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. It is, however, sensitive to the sampling design. The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes. Further investigations are required into less commonly exploited uses of this classifier, such as for sample proximity analysis to detect and remove outliers in the training samples.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 114, April 2016, Pages 24–31
نویسندگان
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