气候变化领域集成服务门户(CCPortal): Land-cover classification with high-resolution remote sensing images using transferable deep models

CCPortal

DOI	10.1016/j.rse.2019.111322
	Land-cover classification with high-resolution remote sensing images using transferable deep models
	Tong X.-Y.; Xia G.-S.; Lu Q.; Shen H.; Li S.; You S.; Zhang L.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	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. © 2019 Elsevier Inc.
英文关键词	Deep learning; Gaofen-2 satellite images; High-resolution remote sensing; land-cover classification
语种	英语
scopus关键词	Classification (of information); Deep learning; Deep neural networks; Image resolution; Image segmentation; Large dataset; Mapping; Neural networks; Remote sensing; Contextual information; Convolutional neural network; Hierarchical segmentation; High resolution remote sensing; High resolution remote sensing images; High spatial resolution; Land cover classification; Satellite images; Image classification; artificial neural network; data set; image analysis; image classification; image resolution; land classification; land cover; mapping method; numerical model; pixel; remote sensing; spectral resolution; supervised classification; China; Jilin
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179543
作者单位	State Key Laboratory LIESMARS, Wuhan University, China; School of Computer Science, Wuhan University, China; Electronic Information School, Wuhan University, China; School of Resource and Environmental Sciences, Wuhan University, China; Key Laboratory of Space Utilization, Tech. & Eng. Center for Space Utilization, Chinese Academy of Sciences, China; Remote Sensing Department, China Land Survey and Planning Institute, China
推荐引用方式 GB/T 7714	Tong X.-Y.,Xia G.-S.,Lu Q.,et al. Land-cover classification with high-resolution remote sensing images using transferable deep models[J],2020,237.
APA	Tong X.-Y..,Xia G.-S..,Lu Q..,Shen H..,Li S..,...&Zhang L..(2020).Land-cover classification with high-resolution remote sensing images using transferable deep models.Remote Sensing of Environment,237.
MLA	Tong X.-Y.,et al."Land-cover classification with high-resolution remote sensing images using transferable deep models".Remote Sensing of Environment 237(2020).