@inproceedings{5ad7ba73791e424b8e0c0419f9f9bdb0,
title = "PLFCN: Pyramid loss reinforced fully convolutional network",
abstract = "In the field of remote sensing, the semantic segmentation network for orthophotos has received widely attention. However, it is usually impossible to achieve high accuracy and high efficiency at the same time. In this paper, we propose a novel pyramid loss reinforced fully convolutional network (PLFCN) to address this issue. By introducing deep pyramid supervisions, the network explores multiscale spatial context information to improve performance of semantic segmentation. And the auxiliary pyramid loss structure can be ignored during testing, so that the network can inference as fast as FCN. The main contributions of this paper are as follows: 1) auxiliary pyramid loss structure is proposed to enhance the performance of FCN by multi-scale and deep supervisions; 2) the advantages of multi scale structures and auxiliary loss is combined to improve the performance and maintain the efficiency at the same time. The results show that the semantic segmentation performance is significantly improved, while achieves the high effeciency as FCN.",
keywords = "FCN, Pyramid Loss, Remote Sensing, Semantic Segmentation",
author = "Shuo Liu and Hongguang Li and Wenrui Ding and Chunlei Liu",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 13th International Conference on Distributed Smart Cameras, ICDSC 2019 ; Conference date: 09-09-2019 Through 11-09-2019",
year = "2019",
month = sep,
day = "9",
doi = "10.1145/3349801.3349819",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
booktitle = "ICDSC 2019 - 13th International Conference on Distributed Smart Cameras",
address = "美国",
}