Inversion Of Chromophoric Dissolved Organic Matter Using Sparse Regression

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)(2019)

引用 0|浏览11
暂无评分
摘要
Chromophoric dissolved organic matter (CDOM) retrieval remains to be a challenging task in water color remote sensing research due to its highly spatial and temporal variability. In this paper, we present a novel CDOM retrieval algorithm that takes advantage of the sparse learning, which can simultaneously perform feature selection and parameter estimation. More specifically, by incorporating the band interaction terms into the original spectral matrix and let it be the basis matrix, then the inversion task can be converted to a classical sparse regression problem, namely LASSO, which can be efficiently solved by the coordinate descend algorithm. Experimental results conducted on both simulated and in-situ datasets have demonstrated the efficiency and superiority of the proposed method over some conventional empirical algorithms.
更多
查看译文
关键词
CDOM retrieval, sparse learning, water quality inversion, basis expansion
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要