土地覆盖
计算机科学
图像分辨率
卷积神经网络
遥感
封面(代数)
人工智能
分辨率(逻辑)
深度学习
代表(政治)
利用
模式识别(心理学)
土地利用
地理
工程类
土木工程
政治
法学
机械工程
计算机安全
政治学
作者
Feng Ling,Giles M. Foody
标识
DOI:10.1080/2150704x.2019.1587196
摘要
Super-resolution mapping (SRM) is a technique to estimate a fine spatial resolution land cover map from coarse spatial resolution fractional proportion images. SRM is often based explicitly on the use of a spatial pattern model that represents the land cover mosaic at the fine spatial resolution. Recently developed deep learning methods have considerable potential as an alternative approach for SRM, based on learning the spatial pattern of land cover from existing fine resolution data such as land cover maps. This letter proposes a deep learning-based SRM algorithm (DeepSRM). A deep convolutional neural network was first trained to estimate a fine resolution indicator image for each class from the coarse resolution fractional image, and all indicator maps were then combined to create the final fine resolution land cover map based on the maximal value strategy. The results of an experiment undertaken with simulated images show that DeepSRM was superior to conventional hard classification and a suite of popular SRM algorithms, yielding the most accurate land cover representation. Consequently, methods such as DeepSRM may help exploit the potential of remote sensing as a source of accurate land cover information.
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