Qiaoyan Fu

China Centre for Resources Satellite Data and Application

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Co-Authors

Chao Wang

Beijing Institute of Big Data Research

Hong Zhang

Aerospace Information Research Institute

Aichun Wang

China Centre for Resources Satellite Data and Application

Yisong Xie

Chinese Academy of Sciences

Zhengjia Zhang

China University of Geosciences

Zhiqiang Pan

Hunan Agricultural University

Kaitao Li

Anhui University

Xiangjun Min

China Centre for Resources Satellite Data and Application

Xuewen Zhang

Nantong University

Zhengqiang Li

Aerospace Information Research Institute

Cooperative Institutions

Chinese Academy of Sciences

Aerospace Information Research Institute

University of Chinese Academy of Sciences

State Key Laboratory of Remote Sensing Science

Institute of Remote Sensing and Digital Earth

China Centre for Resources Satellite Data and Application

Beijing Normal University

Beijing Institute of Big Data Research

Nantong University

Shanghai Jiao Tong University

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[Research on the atmospheric correction for ZY-3 MUX image].

PubMed (2013)

Lei Yang Zhiqiang Pan Qiaoyan Fu Qijin Han Ke Sun

The resolution of the ZY-3 MUX data is 5.8 meter, which is used to the subject classification. It is very difficult to use the dark target method for the atmosphere correction due to the lack of near infrared band in ZY-3 MUX data. The present paper uses the atmospheric correction coefficient look-up table (LUT) constructed by the radiation transmission model 6S based on the aerosol optical depth retrieved from the MODIS data for the atmospheric correction of ZY-3 image. To validate the results, the paper compares the surface spectral curves of the gypsum mine and Gobi and the NDVI values from the corrected and TOA reflectance, the relative error of the atmosphere corrected and the ground-based surface reflectance is less than 6%; the atmospheric correction increases the difference between vegetation NDVI and other features NDVI, highlights vegetation monitoring application ability.

Atmospheric correction

Lookup table

Table (database)

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Citations (3)

Built-Up Area Extraction from GF-3 SAR Data Based on a Dual-Attention Transformer Model

Remote Sensing (2022)

Tianyang Li Chao Wang Fan Wu Hong Zhang Sirui Tian

Built-up area (BA) extraction using synthetic aperture radar (SAR) data has emerged as a potential method in urban research. Currently, typical deep-learning-based BA extractors show high false-alarm rates in the layover areas and subsurface bedrock, which ignore the surrounding information and cannot be directly applied to large-scale BA mapping. To solve the above problems, a novel transformer-based BA extraction framework for SAR images is proposed. Inspired by SegFormer, we designed a BA extractor with multi-level dual-attention transformer encoders. First, the hybrid dilated convolution (HDC) patch-embedding module keeps the surrounding information of the input patches. Second, the channel self-attention module is designed for dual-attention transformer encoders and global modeling. The multi-level structure is employed to produce the coarse-to-fine semantic feature map of BAs. About 1100 scenes of Gaofen-3 (GF-3) data and 200 scenes of Sentinel-1 data were used in the experiment. Compared to UNet, PSPNet, and SegFormer, our model achieved an 85.35% mean intersection over union (mIoU) and 94.75% mean average precision (mAP) on the test set. The proposed framework achieved the best results in both mountainous and plain terrains. The experiments using Sentinel-1 shows that the proposed method has a good generalization ability with different SAR data sources. Finally, the BA map of China for 2020 was obtained with an overall accuracy of about 86%, which shows high consistency with the global urban footprint. The above experiments proved the effectiveness and robustness of the proposed framework in large-scale BA mapping.

10.3390/rs14174182

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Citations (10)

Analysis of ground subsidence at a coal-mining area in Huainan using time-series InSAR

International Journal of Remote Sensing (2015)

Zhengjia Zhang Chao Wang Yixian Tang Hong Zhang Qiaoyan Fu

In Huainan City, Anhui Province, China, ground subsidence persistently occurs due to underground coal mining, which has caused several environmental issues. A modified time-series Interfermetric synthetic aperture radar (InSAR) technique is applied to obtain ground movement in Huainan over a period of approximately two years using 20 ascending Radarsat-2 images. In particular, distributed scatterers (DSs) are efficiently identified using classification information and statistical characteristics. Classified information is used to identify the specific DS classes and statistical characteristics are used to refine the DS candidates. To control error propagation and improve computational efficiency, the deformation rate and digital elevation model (DEM) error of persistent scatterers (PSs) are first retrieved using conventional persistent scatterer intermemetry (PSI). Then, a region-growing-based strategy is applied to extract the deformation rate of DSs. Land subsidence is detected in coal-mining areas of central Dingji, Guqiao, Zhangjiaji, Xinji I, and Xinji II, which suggests that the subsidence is primarily caused by underground mining. The fastest subsidence occurring in non-urban areas is particularly vulnerable to subsidence and collapsed lakes. It is also found that the collapsed lakes have expanded during the observation period, with an expanded mining area of 0.842 km2 near Guqiao. A linear relationship between the observed subsidence and expansion of collapsed lakes was found, which indicates that the expansion of collapsed lakes hass resulted from subsidence due to underground coal mining.

Ground subsidence

10.1080/01431161.2015.1109725

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Citations (28)

Subsidence monitoring in coal area using time-series InSAR combining persistent scatterers and distributed scatterers

International Journal of Applied Earth Observation and Geoinformation (2015)

Zhengjia Zhang Chao Wang Yixian Tang Qiaoyan Fu Hong Zhang

Ground subsidence

10.1016/j.jag.2015.02.007

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Citations (35)

An application of CBERS data in the forest resources investigation of Guizhou Province

Nanjing Linye Daxue xuebao (2005)

Yang Luo Jun Zhu Zhimin Wang Qiaoyan Fu Denghuai Lu

Because of the high mountains, steep slope and fragmentized fields, the spectrum characteristic of target on the ground in Guizhou province is complicated and is heavily disturbed by the phenomenon of same spectrum differnt target and same target different spectrum. It is found that is difficult to reach the ideal result only by the traditional classifying method in the study on the application of CBERS data in the forest resources investigation of Guizhou province. Using the method of non-linear band ratio in the study effectively reduces the disturbance. Analyzing throught grey value, spectrum sectional curve, etc, has generally strengthened the contrast ratio of the image, and the differences between the various land-types, thus improved the classifying precision.

Source

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Citations (2)

[The field radiometric calibration and validation of ZY-3 multispectral sensor].

PubMed (2014)

Xuewen Zhang Qiaoyan Fu Qijin Han Zhiqiang Pan Lei Yang

A field calibration campaign of ZY-3 multispectral sensor (MUS) was performed by the China Center for Resources Satellite Data and Application at the Dunhuang site. The reflectance-based method with two-point sites was used to obtain MUS absolute calibration coefficients in 2013. Compared to the calibration results in 2012, the calibration coefficients in 2013 changed by about 1%-8.5% in different bands. The results were also validated by intercalibration method using the Landsat 8 Operational Land Imager (OLI) data. It shows largely good consistency between field calibration and intercalibration. It was concluded that the absolute calibration coefficients were highly reliable.

Radiometric Calibration

Radiometry

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Citations (2)

Urban Sprawl Study Based on Impervious Surface Percent

Qiaoyan Fu

Impervious surface percent is an important index to indicate urbanization level;it can be used to study the change and spatial extension of urban area quantitatively.Multiple endmember spectral mixture analysis method was used to extract sub-pixel level of impervious surface percent from TM image.This paper applies that method to extract the impervious surface percent of Nanjing Metropolitan development area from two temporal TM images.Then based on impervious surface percent,the change and spatial sprawl of study area was analyzed qualitatively and quantitatively.

Impervious surface

Urban sprawl

Endmember

Built-up area

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Citations (2)

Gaofen-3 sea ice detection based on deep learning

2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL) (2017)

Jinxin Li Chao Wang Shigang Wang Hong Zhang Qiaoyan Fu

Sea ice detection is one of the most important applications in synthetic aperture radar (SAR) image processing and it always be served for ship navigation and climate change studies. Due to the noise, low resolution and multiple characteristics of SAR images, and limitations of traditional SAR image classification methods, the detection accuracy of traditional sea ice SAR images is not high and can't meet the high precision requirements of application in practice. Deep learning, one of the most popular machine learning methods, have been researched and applied in SAR image classification by some researchers. But, so far, very few researchers have applied deep learning methods to sea ice SAR image detection. In this paper, two scenes of Chinese Gaofen-3 SAR data of sea ice were applied to sea ice detection research using convolutional neural network (CNN). First, the train data is obtained from Gaofen-3 sea ice SAR Image by chipping different classes of SAR images into patches; Then, the different classes train data is used to train the CNN and get the trained CNN model; After the training, the trained CNN model was used to classify the Gaofen-3 sea ice SAR image with sea ice and non-sea ice by a patch-based window traversing the entire SAR image. In order to illustrate the sea ice detection result of deep learning, several traditional SAR image detection methods are experimented for contrast. Experimental results demonstrate that deep learning method is suitable for sea ice SAR image detection and achieves high detection accuracy than others traditional methods.

10.1109/piers-fall.2017.8293267

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Citations (30)

New Progress in Land Use/Land Cover Change Detection by Remote Sensing

Remote Sensing Information (2006)

Qiaoyan Fu

Land use / land cover change detection is an important aspect in resource and environment researches.Precise and timely change information will provide the decision-making support for the production and management of human resource and environment.Remote sensing technique has the characteristics of real time,rapidity,wide cover range,multi-spectrum and periodicity,which provide the perfect data foundation for land use / land cover change detection.Consequently,the development of human society has put the new demands on the precision and efficiency of land use / land cover change detection.Thus,some new techniques and methods are introduced into those areas.The new techniques and methods raise the precision of change detection;while their efficiencies still wait for the study.

Land Cover

Land information system

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Citations (5)

Validation of MODIS Aerosol Optical Depth Retrieval over Mountains in Central China Based on a Sun-Sky Radiometer Site of SONET

Remote Sensing (2016)

Yan Ma Zhengqiang Li Zhaozhou Li Yisong Xie Qiaoyan Fu

The 3 km Dark Target (DT) aerosol optical depth (AOD) products, 10 km DT and Deep Blue (DB) AOD products from the Collection 6 (C6) product data of Moderate Resolution Imaging Spectroradiometer (MODIS) are compared with Sun-sky Radiometer Network (SONET) measurements at Song Mountain in central China, where ground-based remote sensing measurements of aerosol properties are still very limited. The seasonal variations of AODs are significant in the Song Mountain region, with higher AODs in spring and summer and lower AODs in autumn and winter. Annual mean AODs (0.55 µm) vary in the range of 0.5–0.7, which indicates particle matter (PM) pollutions in this mountain region. Validation against one-year ground-based measurements shows that AOD retrievals from the MODIS onboard Aqua satellite are better than those from the Terra satellite in Song Mountain. The 3 km and 10 km AODs from DT algorithms are comparable over this region, while the AOD accuracy of DB algorithm is relatively lower. However, the spatial coverage of DB products is higher than that of 10 km DT products. Moreover, the optical and microphysical characteristics of aerosols at Song Mountain are analyzed on the basis of SONET observations. It suggests that coarse-mode aerosol particles dominate in spring, and fine-mode particles dominate in summer. The aerosol property models are also established and compared to aerosol types used by MODIS algorithm.

AERONET

Moderate-resolution imaging spectroradiometer

Deep blue

10.3390/rs8020111

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Citations (41)