.png){kind=icon}
返回
Scaling Up SLIC Superpixels Using a Tile-Based Approach
.png){kind=icon}
OA
AI
DOI：10.1109/TGRS.2018.2880248.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Image segmentation techniques are challenging to apply to large-size remote sensing imagery. Indeed, if the data to be processed are larger than the computer's available memory, it must be split into smaller pieces. Without precaution, segmentation errors appear along the edges of these pieces. The goal of this paper is to present a tilewise processing method to overcome this issue for superpixel segmentation, applied in particular to the simple linear iterative clustering algorithm. Incidentally, tilewise methods allow for several pieces of the image to be processed simultaneously, which enables the deployment of these methods in a parallel processing environment. Estimations of the speed-up when using multiple processors are provided. Then, it is demonstrated that the result of the tilewise segmentation is equivalent to the segmentation of the complete image, with respect to a number of global unsupervised segmentation criteria. Finally, experimental results on a large-size Sentinel-2 time series validate the method's feasibility.
Keyword:
Image processing
image segmentation
scalability
superpixel segmentation
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks基于卷积神经网络的亚分米分辨率图像密集语义标注
OAAI
.png){kind=icon}
OAAI分享收藏Health Literacy – a review of research using the European Health Literacy Questionnaire (HLS-EU-Q16) in 2010-2018健康素养-2010-2018使用欧洲健康素养问卷 (HLS-EU-Q16) 进行的研究综述OAAI
OAAI分享收藏
查看更多内容