One Model Is Enough: Toward Multiclass Weakly Supervised Remote Sensing Image Semantic Segmentation

计算机科学 人工智能 分割 过度拟合 像素 图像分割 公制(单位) 模式识别(心理学) 计算机视觉 遥感 人工神经网络 地理 运营管理 经济
作者
Zhenshi Li,Xueliang Zhang,Pengfeng Xiao
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-13 被引量:14
标识
DOI:10.1109/tgrs.2023.3290242
摘要

Semantic segmentation of remote sensing images is effective for large-scale land cover mapping, which heavily relies on a large amount of training data with laborious pixel-level labeling. Weakly supervised semantic segmentation (WSSS) based on image-level labels has attracted intensive attention due to its easy availability. However, existing image-level WSSS methods for remote sensing images mainly focus on binary segmentation, which are difficult to apply to multiclass scenarios. This study proposes a comprehensive framework for image-level multiclass WSSS of remote sensing images, consisting of appropriate image-level label generation, high-quality pixel-level pseudo mask generation, and segmentation network iterative training. Specifically, a training sample filtering method, as well as a dataset cooccurrence evaluation metric, is proposed to demonstrate proper image-level training samples. Leveraging multiclass class activation maps, an uncertainty-driven pixel-level weighted mask is proposed to relieve the overfitting of labeling noise in pseudo masks when training the segmentation network. Extensive experiments demonstrate that the proposed framework can achieve high-quality multiclass WSSS performance with image-level labels, which can attain 94.23% and 90.77% of the IoUs from pixel-level labels for the ISPRS Potsdam and Vaihingen datasets, respectively. Beyond that, for the DeepGlobe dataset with more complex landscapes, the WSSS framework can achieve an accuracy close to 99% of the fully supervised case. Additionally, we further demonstrate that compared to adopting multiple binary WSSS models, directly training a multiclass WSSS model can achieve better results, which can provide new thoughts to achieve WSSS of remote sensing images for multiclass application scenarios. Our code is public at https://github.com/NJU-LHRS/OME.
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
影子发布了新的文献求助10
刚刚
赘婿应助蓝桉采纳,获得10
刚刚
1秒前
多年以后完成签到,获得积分10
1秒前
goldNAN发布了新的文献求助10
1秒前
激动的访文完成签到,获得积分10
1秒前
NexusExplorer应助咩咩采纳,获得10
1秒前
蓝莓松饼完成签到,获得积分20
1秒前
1秒前
未来的枕头完成签到,获得积分10
2秒前
2秒前
3秒前
3秒前
3秒前
爱你完成签到,获得积分10
3秒前
lxy发布了新的文献求助10
3秒前
薛蹇发布了新的文献求助10
4秒前
4秒前
4秒前
4秒前
小余同学发布了新的文献求助10
5秒前
烟花应助rengar采纳,获得10
5秒前
6秒前
6秒前
6秒前
wasd完成签到,获得积分20
6秒前
mumu发布了新的文献求助10
7秒前
7秒前
Orange应助晨陌兮客采纳,获得10
7秒前
qyzhu发布了新的文献求助10
7秒前
Lin发布了新的文献求助10
7秒前
8秒前
8秒前
xixi发布了新的文献求助20
8秒前
能干的邹发布了新的文献求助10
8秒前
Lynn发布了新的文献求助10
8秒前
坚果发布了新的文献求助10
9秒前
MHX发布了新的文献求助10
10秒前
汉堡包应助手可摘柠檬采纳,获得10
10秒前
HAPT发布了新的文献求助10
10秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 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 3970632
求助须知:如何正确求助?哪些是违规求助? 3515261
关于积分的说明 11177794
捐赠科研通 3250448
什么是DOI,文献DOI怎么找? 1795314
邀请新用户注册赠送积分活动 875781
科研通“疑难数据库(出版商)”最低求助积分说明 805073