.png){kind=icon}
返回
Deep-learning-based solution for data deficient satellite image segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2021.116210.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Satellite images have become more easily accessible due to government programmes and the availability of commercial earth observation satellites. The increased volume of data and the recent advancement in deep learning-based techniques have made possible the development of fast and accurate methods for satellite image segmentation, which aims at automatically giving each pixel in the satellite image a label of the underlying object/ground type. However, despite the abundant supply of raw satellite images, the labelling of these images is a manual task and is therefore time and labour intensive. When a large labelled dataset is absent, resorting to a smaller data source significantly hampers the performance of the machine learning model. Therefore, an approach which mitigates the effects of using small labelled datasets should be investigated. This paper provides a solution to the problem of insufficient data samples for satellite image segmentation. To this end, two methods are developed and compared with the benchmark U-Net model: the use of transfer learning on an Xception architecture, and the use of steerable filters. The first model transfers knowledge from a pre-trained Xception model and predicts with a multi-resolution feature fusion module, specifically designed to recover fine details. The latter further improves the capability of our neural network model in handling data with high rotational variation. Both models are experimentally shown to significantly enhance the segmentation performance despite a small-sized training dataset.
Keyword:
Satellite image
Segmentation
Deep learning
Neural networks
Transfer learning
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
Distant regulatory elements in a Sox10‐βGEO BAC transgene are required for expression of Sox10 in the enteric nervous system and other neural crest‐derived tissuesSox10-βgeo BAC转基因中的远距离调控元件是肠神经系统和其他神经源性组织中 Sox10 表达所必需的
OAAI
OAAI分享收藏Market orientation and internet-related cognitions: inside the minds of small business managers
PREAI
分享收藏Structural study of lanthanides(III) in aqueous nitrate and chloride solutions by EXAFS通过EXAFS对硝酸盐和氯化物水溶液中镧系元素 (III) 的结构研究
PREAI
分享收藏
查看更多内容