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
zhaomr完成签到,获得积分10
1秒前
1秒前
3秒前
独特冰安发布了新的文献求助10
3秒前
清秀乾完成签到,获得积分10
4秒前
cAMP发布了新的文献求助10
4秒前
李健的小迷弟应助rym0404采纳,获得10
5秒前
6秒前
探寻发布了新的文献求助10
7秒前
8秒前
8秒前
打打应助幸运小怪兽采纳,获得10
9秒前
白许四十完成签到,获得积分10
9秒前
13秒前
sopha发布了新的文献求助10
13秒前
tian完成签到,获得积分10
14秒前
wangmeiqiong发布了新的文献求助10
14秒前
18秒前
19秒前
JamesPei应助研友_Z1WrgL采纳,获得10
20秒前
幸运小怪兽完成签到,获得积分10
22秒前
陈佳完成签到,获得积分20
22秒前
酷炫的幻丝完成签到 ,获得积分10
22秒前
23秒前
SSSSCCCCIIII完成签到,获得积分10
23秒前
rym0404发布了新的文献求助10
24秒前
hilbertbo发布了新的文献求助10
24秒前
25秒前
ssstuck完成签到,获得积分10
25秒前
HYT发布了新的文献求助50
27秒前
ping发布了新的文献求助10
28秒前
29秒前
31秒前
失眠的狗发布了新的文献求助30
33秒前
35秒前
探寻发布了新的文献求助10
36秒前
37秒前
研友_VZG7GZ应助公孙世往采纳,获得10
37秒前
量子星尘发布了新的文献求助10
37秒前
奶昔发布了新的文献求助10
39秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
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 3976418
求助须知:如何正确求助?哪些是违规求助? 3520512
关于积分的说明 11203586
捐赠科研通 3257127
什么是DOI,文献DOI怎么找? 1798594
邀请新用户注册赠送积分活动 877804
科研通“疑难数据库(出版商)”最低求助积分说明 806523