Semi-supervised support vector regression model for remote sensing water quality retrieving |
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Authors: | Xili Wang Li Fu Lei Ma |
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Institution: | (1) Exagen Diagnostics, Inc. Houston, TX, USA;(2) Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA |
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Abstract: | This paper proposed a semi-supervised regression model with co-training algorithm based on support vector machine, which was
used for retrieving water quality variables from SPOT5 remote sensing data. The model consisted of two support vector regressors
(SVRs). Nonlinear relationship between water quality variables and SPOT5 spectrum was described by the two SVRs, and semi-supervised
co-training algorithm for the SVRs was established. The model was used for retrieving concentrations of four representative
pollution indicators—permanganate index (CODmn), ammonia nitrogen (NH3-N), chemical oxygen demand (COD) and dissolved oxygen (DO) of the Weihe River in Shaanxi Province, China. The spatial distribution
map for those variables over a part of the Weihe River was also produced. SVR can be used to implement any nonlinear mapping
readily, and semi-supervised learning can make use of both labeled and unlabeled samples. By integrating the two SVRs and
using semi-supervised learning, we provide an operational method when paired samples are limited. The results show that it
is much better than the multiple statistical regression method, and can provide the whole water pollution conditions for management
fast and can be extended to hyperspectral remote sensing applications. |
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Keywords: | semi-supervised learning support vector regression co-training water quality retrieving model SPOT 5 |
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