Prof. Shufang Tian Profile

Prof. Shufang Tian

Associate Professor at China Univ of Geosciences

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Author

Publications (4)

SPIE Journal Paper | 2 December 2020

Comparison of pixel- and object-based image analysis for tea plantation mapping using hyperspectral Gaofen-5 and synthetic aperture radar data

Yujia Chen, Shufang Tian

JARS, Vol. 14, Issue 04, 044516, (December 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.044516

KEYWORDS: Synthetic aperture radar, Associative arrays, Image analysis, Image classification, Image segmentation, Image fusion, Vegetation, Backscatter, Data fusion, Remote sensing

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Accurately mapping tea plantation distribution is crucial to environmental protection and sustainable development. Hyperspectral and synthetic aperture radar (SAR) data have recently been widely used in land cover classification, but their ability to extract tea plantation regions still needs to be confirmed. Compared with traditional pixel-based image analysis (PBIA), object-based image analysis (OBIA) for tea plantation mapping is more worthy of implementation. This study explored the performance of Gaofen-5 (GF-5) and copolarized SAR data for tea plantation mapping using pixel- and object-based support vector machine algorithms in Wuyishan, China. Comparison of PBIA and OBIA demonstrated the significant differences in visual effect and classification accuracy. The object-based classifications especially offered a more contiguous depiction with fewer speckles of tea plantations than pixel-based classifications did. Moreover, object-based classifications improved overall accuracy (OA) between 1.7% and 7.9% in all scenarios when compared to pixel-based classifications. As for datasets, classifications using only GF-5 data obtained an OA of over 85%, while fusing images decreased classification accuracy due to the lower separability between tea and forest, showcasing that the fusion of hyperspectral and SAR data does not guarantee the improvement of classification accuracy. The integration of GF-5, horizontal transmit and horizontal receive (HH), and vertical transmit and vertical receive (VV) polarized data outperformed other data combinations in both pixel- and object-based classifications and achieved the highest OA (95.58%) in OBIA, with a 98.13% producer accuracy and 93.34% user accuracy of tea plantations. The results indicated OBIA could overcome the shortcomings of the PBIA and effectively improve the mapping accuracy of tea plantation, and OBIA integrating GF-5, HH, and VV polarized data could play a distinguished role in tea plantation mapping. This work provides a promising approach for mapping tea plantations and demonstrates that the integration of GF-5 and copolarized data can improve spectral separability of vegetation, which is also significant for general forest mappings.

SPIE Journal Paper | 10 June 2016

Temperature and emissivity separation via sparse representation with thermal airborne hyperspectral imager data

Chengyi Li, Shufang Tian, Shijie Li, Mei Yin

JARS, Vol. 10, Issue 04, 042003, (June 2016) https://doi.org/10.1117/12.10.1117/1.JRS.10.042003

KEYWORDS: Associative arrays, Algorithm development, Hyperspectral imaging, Atmospheric corrections, Imaging systems, Temperature metrology, Remote sensing, Vegetation, Lithium, Spectral resolution

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Proceedings Article | 24 November 2008 Paper

Remote sensing survey of surface water environment in urban areas: cases in Beijing, Kunming, and Wuhan

Ruisi Zhang, Shufang Tian

Proceedings Volume 7123, 71230T (2008) https://doi.org/10.1117/12.816190

KEYWORDS: Remote sensing, Pollution, Environmental sensing, Water contamination, Infrared radiation, Infrared imaging, Vegetation, Visualization, Data acquisition, RGB color model

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Proceedings Article | 24 November 2008 Paper

The information of oil and gas micro-seepage in Dongsheng region of inner Mongolia based on the airborne hyperspectral remote sensing image

Shu-Fang Tian, Jian-Ping Chen, Mi Zhou

Proceedings Volume 7123, 71230K (2008) https://doi.org/10.1117/12.816179

KEYWORDS: Remote sensing, Minerals, Image quality, Carbonates, Reflectivity, Mining, Data conversion, Feature extraction, Image processing, Hyperspectral imaging

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