Land-cover classification with high-resolution remote sensing images using transferable deep models

计算机科学 土地覆盖 卷积神经网络 遥感 人工智能 可转让性 模式识别(心理学) 上下文图像分类 分割 深度学习 图像分辨率 像素 土地利用 数据挖掘 封面(代数) 图像(数学) 机器学习 地理 罗伊特 土木工程 工程类 机械工程
作者
Xinyi Tong,Gui-Song Xia,Qikai Lu,Huanfeng Shen,Shengyang Li,Shucheng You,Liangpei Zhang
出处
期刊:Remote Sensing of Environment [Elsevier BV]
卷期号:237: 111322-111322 被引量:684
标识
DOI:10.1016/j.rse.2019.111322
摘要

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images.
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
顺shun完成签到 ,获得积分10
刚刚
情怀应助huayi采纳,获得10
1秒前
djiwisksk66应助南佳采纳,获得10
1秒前
2秒前
Albertxkcj发布了新的文献求助10
2秒前
hui发布了新的文献求助10
2秒前
无花果应助奇异喵采纳,获得10
3秒前
3秒前
Gudeguy完成签到 ,获得积分10
5秒前
6秒前
6秒前
点点完成签到,获得积分10
7秒前
顺利纸飞机完成签到,获得积分10
7秒前
粽粽发布了新的文献求助20
8秒前
脑洞疼应助LL采纳,获得10
9秒前
无限的可乐完成签到,获得积分10
9秒前
orixero应助zhang005on采纳,获得10
10秒前
Orange应助leon采纳,获得10
10秒前
Lu发布了新的文献求助10
10秒前
cherry发布了新的文献求助10
12秒前
小二郎应助hui采纳,获得10
12秒前
Akim应助163采纳,获得10
12秒前
隐形曼青应助lcs24201002032采纳,获得10
12秒前
12秒前
xiangoak发布了新的文献求助20
13秒前
豆花发布了新的文献求助10
13秒前
田様应助ZCC采纳,获得10
14秒前
14秒前
15秒前
15秒前
斤斤发布了新的文献求助10
16秒前
hailey53完成签到,获得积分10
16秒前
5441588关注了科研通微信公众号
17秒前
17秒前
美满忆安完成签到,获得积分10
17秒前
17秒前
羊丢丢啊丢丢完成签到,获得积分10
18秒前
认真的映安完成签到,获得积分10
18秒前
天玄一刀发布了新的文献求助10
18秒前
NexusExplorer应助优秀新烟采纳,获得30
19秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 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
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 3969557
求助须知:如何正确求助?哪些是违规求助? 3514377
关于积分的说明 11173836
捐赠科研通 3249692
什么是DOI,文献DOI怎么找? 1794979
邀请新用户注册赠送积分活动 875537
科研通“疑难数据库(出版商)”最低求助积分说明 804836