IEEE JSTARS: Evaluation of feature ranking and regression methods for oceanic chlorophyll-a estimation
Katalin Blix and Torbjørn Eltoft’s paper on “Evaluation of feature ranking and regression methods for oceanic chlorophyll-a estimation” has been accepted for publication in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
This paper evaluates two alternative regression techniques for oceanic Chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian Process Regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely Sensitivity Analysis (SA) and Automatic Relevance Determination (ARD). We also investigate a second regression method, the Partial Least Squares Regression (PLSR) for oceanic Chl-a content estimation. Feature relevance in the PLSR model can be accessed through the Variable Importance in Projection (VIP) feature ranking algorithm. The paper thus analyses three feature ranking models, SA, ARD and VIP, which are all derived from different fundamental principles, and uses the ranked features as inputs to the GPR and PLSR to assess regression strengths. We compare the regression performances using some common performance measures, and show how the feature ranking methods can be used to find the lowest number of features to estimate oceanic Chl-a content by using the GPR and PLSR models, while still producing comparable performance to the state-of art algorithms. We evaluate the models on a global MERIS matchup dataset. Our results show that the GPR model has the best regression performance for most of the input feature sets we used, and our conclusion is this model can favorably be used for Chl-a content retrieval, already with two features, ranked by the either the SA or ARD methods.
Blix, K., Eltoft, T. (accepted): Evaluation of feature ranking and regression methods for oceanic chlorophyll-a estimation. Accepted for publication in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. [intranet]