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首页> 美国政府科技报告 >Machine Learning Approaches for High-Resolution Urban Land Cover Classification. (A Comparative Study).
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Machine Learning Approaches for High-Resolution Urban Land Cover Classification. (A Comparative Study).

机译：高分辨率城市土地覆盖分类的机器学习途径。 （比较研究）。

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摘要

The proliferation of several machine learning approaches makes it difficult to identify a suitable classification technique for analyzing high-resolution remote sensing images. In this study, ten classification techniques were compared from five broad machine learning categories. Surprisingly, the performance of simple statistical classification schemes like maximum likelihood and Logistic regression over complex and recent techniques is very close. Given that these two classifiers require little input from the user, they should still be considered for most classification tasks. Multiple classifier systems is a good choice if the resources permit. Remote sensing, which provides inexpensive, synoptic-scale data with multi-temporal coverage, has proven to be very useful in land cover mapping, environmental monitoring, and forest and crop inventory.

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