Built-Up Area Extraction From GF-3 Image based on an Improved Transformer Model

计算机科学 人工智能 特征提取 分割 图像分割 模式识别(心理学) 卷积神经网络 计算机视觉
作者
Tianyang Li,Chao Wang,Fan Wu,Hong Zhang,Bo Zhang,Lu Xu
标识
DOI:10.1109/igarss46834.2022.9884924
摘要

With the development of urbanisation in China, the urban areas are expanding rapidly, but there is a huge regional disparity between the east, central and western regions. The urban development in the western region lags far behind that in the eastern and central regions. In the western region of China, due to the large number of mountains and SAR backscatter mechanism, there are a lot of overlays in the image, resulting in high false alarms in built-up areas segmentation. In order to solve the problem, this paper proposed a new built-up area extraction model based on the Transformer. Different from the segmentation method based on convolutional neural network, the self-attention mechanism of the Transformer was introduced to effectively capture the image context information and reduce the impact of mountain overlays on the extraction of built-up areas. The multi-layer Transformer encoder and the multilayer perceptron (MLP) decoder were used to fuse feature maps of different scales for the sake of enhancing the ability to extract architectural features. With the purpose of improving the generalization ability, various data augmentation methods were used during training, such as random noise, random blur, and random distortion. In this paper, about 32000 samples, including some mountainous areas and around China, were used for training, which are from 27 scenes of GF-3 10m SAR images covering different areas in China. The method proposed in this paper reached a mIoU of 0.8130 and a Kappa coefficient of 0.9423, which significantly reduced false alarms in mountainous areas. Taking the study area of Lanzhou City, Gansu Province of China as an example, the result is basically consistent with the classification map of World Cover. It shows that the proposed method has a good ability to extract the distribution information of built-up areas.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小马甲应助yuanshl1985采纳,获得10
刚刚
zhuxiaonian完成签到,获得积分10
3秒前
田様应助淘气科研采纳,获得10
3秒前
chenyi完成签到,获得积分10
4秒前
zyyyy完成签到,获得积分10
4秒前
奶黄包完成签到 ,获得积分10
4秒前
SYLH应助海阔天空采纳,获得10
4秒前
4秒前
机灵又蓝完成签到,获得积分10
5秒前
张土豆完成签到 ,获得积分10
5秒前
善学以致用应助小王采纳,获得10
5秒前
orang完成签到,获得积分10
6秒前
巧巧艾完成签到,获得积分10
6秒前
7秒前
邵洋完成签到,获得积分10
7秒前
sl发布了新的文献求助10
7秒前
8秒前
小迪迦奥特曼完成签到,获得积分10
8秒前
8秒前
cckk发布了新的文献求助10
9秒前
夏目由美完成签到 ,获得积分10
9秒前
Jasper应助哦哦哦采纳,获得10
10秒前
YYD完成签到,获得积分10
10秒前
超勍完成签到,获得积分10
10秒前
碧蓝碧凡发布了新的文献求助10
11秒前
Popeye应助鹤鸣采纳,获得30
11秒前
YYD发布了新的文献求助10
12秒前
yuanshl1985发布了新的文献求助10
12秒前
积极的黑猫完成签到,获得积分10
13秒前
GB完成签到 ,获得积分10
13秒前
Metx完成签到 ,获得积分10
14秒前
孤独的涔完成签到,获得积分10
15秒前
Jay完成签到,获得积分10
15秒前
16秒前
深情安青应助hf采纳,获得10
18秒前
学不懂数学应助大观天下采纳,获得10
18秒前
醉熏的水绿完成签到 ,获得积分10
18秒前
秦艺完成签到,获得积分10
19秒前
19秒前
19秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038426
求助须知:如何正确求助?哪些是违规求助? 3576119
关于积分的说明 11374556
捐赠科研通 3305834
什么是DOI,文献DOI怎么找? 1819339
邀请新用户注册赠送积分活动 892678
科研通“疑难数据库(出版商)”最低求助积分说明 815029