Extraction Method of Urban Area From GF-6 Satellite

Changdong Ji; Zhonglin Kang

doi:10.3788/LOP202158.0428003

[1] Liu Y. The study on the expansion of urban built-up area based on Envi: a case study of Jiangjin district of Chongqing city[J]. Anhui Agricultural Science Bulletin, 25, 150-153(2019).

[2] Tong B. Research on urban built-up areas extraction based on multi-resources remote sensing data[J]. Journal of Liaoning Provincial College of Communications, 20, 28-31(2018).

[3] Wang R X, Li J, Li X et al. Extraction of built-up areas using combination of Landsat and DMSP data: a case study of Jiangxi province[J]. Journal of Central China Normal University (Natural Sciences), 52, 130-136, 146(2018).

[4] Zheng H H, Gui Z P, Li F et al. Urban built-up area extraction method based on nighttime light images and point of interest data[J]. Geography and Geo-Information Science, 35, 25-32(2019).

[5] Li F, Yan Q W, Zou Y J et al. Using POI to improve the accuracy of night light data extraction to establish zone boundaries: taking the 01 Luojia satellite and NPP / VIIRS data as an example[J]. Geomatics and Information Science of Wuhan University, 14, 11-13(2019).

[6] Liu Z L, Zhang Q B, Yue D P et al. Extraction of urban built-up areas based on Sentinel-2A and NPP-VIIRS nighttime light data[J]. Remote Sensing for Land & Resources, 31, 227-234(2019).

[7] Li D R, Li X. An overview on data mining of nighttime light remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 44, 591-601(2015).

[8] Zhong S Y, Li X X, Bai Y H et al. The method of extracting built-up areas based on multi-scale segmentation and spectral features[J]. Software Guide, 17, 180-184(2018).

[9] Tong B, Shen W. Object-oriented Landsat 8 image of the city proper extraction method research[J]. Journal of Liaoning Provincial College of Communications, 19, 21-25(2017).

[10] Dams J, Dujardin J, Reggers R et al. Mapping impervious surface change from remote sensing for hydrological modeling[J]. Journal of Hydrology, 485, 84-95(2013).

[11] Deng C B, Wu C S. BCI: a biophysical composition index for remote sensing of urban environments[J]. Remote Sensing of Environment, 127, 247-259(2012). http://www.sciencedirect.com/science/article/pii/S003442571200363X

[12] Fan F L, Deng Y B, Hu X F et al. Estimating composite curve number using an improved SCS-CN method with remotely sensed variables in Guangzhou, China[J]. Remote Sensing, 5, 1425-1438(2013).

[13] Risky Y S, Aulia Y H, Widayani P. Spatiotemporal built-up land density mapping using various spectral indices in Landsat-7 ETM+ and Landsat-8 OLI/TIRS (case study: Surakarta City)[J]. IOP Conference Series Earth and Environmental Science, 98, 012006(2017). http://adsabs.harvard.edu/abs/2017E%26ES...98a2006R

[14] Estoque R C, Murayama Y. Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: a comparative assessment of various spectral indices[J]. Ecological Indicators, 56, 205-217(2015). http://www.sciencedirect.com/science/article/pii/S1470160X15001673

[15] Bai Y Q, He G J, Wang G Z et al. WE-NDBI-A new index for mapping urban built-up areas from GF-1 WFV images[J]. Remote Sensing Letters, 11, 407-415(2020).

[16] Tang C, Ling Y S, Yang H et al. Decision-level fusion tracking for infrared and visible spectra based on deep learning[J]. Laser & Optoelectronics Progress, 56, 071502(2019).

[17] Li S Y, Liu Y H, Zhang R F. Fine-grained image classification based on multi-scale feature fusion[J]. Laser & Optoelectronics Progress, 57, 121002(2020).

[18] Zhang Q, Dong A G, Song R. Hyperspectral image classification based on multiple features and an improved autoencoder[J]. Laser & Optoelectronics Progress, 57, 081010(2020).

[19] Deng S B, Wu H G, Jiang T. A study on relative radiometric normalization for remote sensing dynamic monitoring[J]. Remote Sensing Information, 23, 71-75(2008).

[20] Zhou K, Chen L Y, Shen X J et al. Impervious surface extraction of Kaifeng urban area based on Landsat-8[J]. Geospatial Information, 17, 88-91, 115(2019).