计算机科学
卷积神经网络
人工智能
上下文图像分类
模式识别(心理学)
图像分辨率
图像(数学)
功能(生物学)
特征提取
计算机视觉
机器学习
进化生物学
生物
作者
Emmanuel Maggiori,Yuliya Tarabalka,Guillaume Charpiat,Pierre Alliez
标识
DOI:10.1109/igarss.2017.8128163
摘要
We address the pixelwise classification of high-resolution aerial imagery. While convolutional neural networks (CNNs) are gaining increasing attention in image analysis, it is still challenging to adapt them to produce fine-grained classification maps. This is due to a well-known trade-off between recognition and localization: the impressive capability of CNNs to recognize meaningful objects comes at the price of losing spatial precision. We here propose an architecture that addresses this issue. It learns features at different levels of detail and also learns a function to combine them. By integrating local and global information in an efficient and flexible manner, it outperforms previous techniques.
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