Comparison of water extraction methods in Tibet based on GF-1 data

Lingjun Jia; Kun Shang; Jing Liu; Zhongqing Sun

doi:10.1117/12.2288734

8 March 2018 Comparison of water extraction methods in Tibet based on GF-1 data

Lingjun Jia, Kun Shang, Jing Liu, Zhongqing Sun

Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106110P (2018) https://doi.org/10.1117/12.2288734
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

In this study, we compared four different water extraction methods with GF-1 data according to different water types in Tibet, including Support Vector Machine (SVM), Principal Component Analysis (PCA), Decision Tree Classifier based on False Normalized Difference Water Index (FNDWI-DTC), and PCA-SVM. The results show that all of the four methods can extract large area water body, but only SVM and PCA-SVM can obtain satisfying extraction results for small size water body. The methods were evaluated by both overall accuracy (OAA) and Kappa coefficient (KC). The OAA of PCA-SVM, SVM, FNDWI-DTC, PCA are 96.68%, 94.23%, 93.99%, 93.01%, and the KCs are 0.9308, 0.8995, 0.8962, 0.8842, respectively, in consistent with visual inspection. In summary, SVM is better for narrow rivers extraction and PCA-SVM is suitable for water extraction of various types. As for dark blue lakes, the methods using PCA can extract more quickly and accurately.

Citation Download Citation

Lingjun Jia, Kun Shang, Jing Liu, and Zhongqing Sun "Comparison of water extraction methods in Tibet based on GF-1 data", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106110P (8 March 2018); https://doi.org/10.1117/12.2288734

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KEYWORDS

Principal component analysis

Image fusion

Data modeling

Satellites

Earth observing sensors

Near infrared

Atmospheric modeling

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