TY - GEN
T1 - Hyperspectral inversion for soil moisture and temperature based on Gaussian process regression
AU - Li, Zhen
AU - Deng, Chenwei
AU - Zhao, Baojun
AU - Tian, Yibing
AU - Huang, Yun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - The soil moisture and temperature significantly influence the natural environment. Hyperspectral remote sensing can serve as a pivotal technique to monitor soil surface. However, modeling soil parameters encounter the following problems: hyperspectral data is high-dimensional and non-linear, and hyperspectral datasets are of limited size. In this paper, we derive a framework for inversion of soil moisture and temperature. First, wavelet transform is adopted that is able to extract the main structure of spectrum curve and reduce the dimensionality of the hyperspectral data. Then, Gaussian process regression (GPR), which is suitable for small sample data, is applied to predict the soil moisture and temperature. The experimental results show that our model outperforms other methods in estimating soil character.
AB - The soil moisture and temperature significantly influence the natural environment. Hyperspectral remote sensing can serve as a pivotal technique to monitor soil surface. However, modeling soil parameters encounter the following problems: hyperspectral data is high-dimensional and non-linear, and hyperspectral datasets are of limited size. In this paper, we derive a framework for inversion of soil moisture and temperature. First, wavelet transform is adopted that is able to extract the main structure of spectrum curve and reduce the dimensionality of the hyperspectral data. Then, Gaussian process regression (GPR), which is suitable for small sample data, is applied to predict the soil moisture and temperature. The experimental results show that our model outperforms other methods in estimating soil character.
KW - Gaussian process regression
KW - hyperspectral inversion
KW - soil character
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85091930106&partnerID=8YFLogxK
U2 - 10.1109/ICSIDP47821.2019.9172823
DO - 10.1109/ICSIDP47821.2019.9172823
M3 - Conference contribution
AN - SCOPUS:85091930106
T3 - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
BT - ICSIDP 2019 - IEEE International Conference on Signal, Information and Data Processing 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2019
Y2 - 11 December 2019 through 13 December 2019
ER -