Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data

Kwiatkowska, Ewa; Fargion, Giulietta S.

doi:10.1109/tgrs.2003.818016

Cited by 75 publications

(37 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The support vector regression (SVR) is the SVM implementation for regression and function approximation [5,54], which has yielded good results in modeling some biophysical parameters and in alleviating the aforementioned problems of neural networks [55,56,11].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

A Review of Kernel Methods in Remote Sensing Data Analysis

Gómez-Chova

Muñoz-Marí

Laparra

et al. 2011

Optical Remote Sensing

View full text Add to dashboard Cite

Summary. Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastructures, agriculture inventorying, disaster prevention and damage assessment, anomaly and target detection, biophysical parameter estimation, band selection, and feature extraction.This chapter provides a survey of applications and recent theoretical developments of kernel methods in the context of remote sensing data analysis. The specific methods developed in the fields of supervised classification, semisupervised classification, target detection, model inversion, and nonlinear feature extraction are revised both theoretically and through experimental (illustrative) examples. The emergent fields of transfer, active, and structured learning, along with efficient parallel implementations of kernel machines, are also revised.

show abstract

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

A Review of Kernel Methods in Remote Sensing Data Analysis

Gómez-Chova

Muñoz-Marí

Laparra

et al. 2011

Optical Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Behrenfeld et al 2006;Martinez et al 2009;Mélin 2009, 2011) but the inclusion of other subsequent missions in the overall data record is now necessary to extend the temporal basis of such analyses. Several investigations for local or global applications actually used the SeaWiFS record with data from other missions (McClain, Signorini, and Christian 2004;Djavidnia 2009, Mélin et al 2011;Kahru et al 2012;Bélanger, Babin, and Tremblay 2013;Coppini et al 2013;Saulquin et al 2013;Gregg and Casey 2010;Gregg and Roussseaux 2014;Park et al 2015;Signorini, Franz, and McClain 2015), and various merging techniques were proposed to combine data sets from multiple missions (Kwiatkowska and Fargion 2003;Maritorena and Siegel 2005;Pottier et al 2006;Mélin et al 2011;IOCCG 2007).…”

Section: Introductionmentioning

confidence: 99%

Impact of inter-mission differences and drifts on chlorophyll-atrend estimates

Mélin

2016

International Journal of Remote Sensing

View full text Add to dashboard Cite

The chlorophyll-a (chl-a) concentration is an Essential Climate Variable, and the study of its variability at global scale requires a succession of satellite ocean colour missions to cover a period suitable for climate research. In the context of a multi-mission data record, inter-mission differences can introduce artefacts affecting trend evaluations, and the impact of the bias between the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) chl-a products is shown to be significant in a substantial part of the ocean. The assessment of trends can also be directly impacted by a drift in the chl-a time series resulting from sensor functions. These issues are addressed by a sensitivity analysis that compares slopes of linear regression obtained for varying levels of intermission bias and drift with respect to a 15-year reference series built with SeaWiFS and MODIS data. The relationship, constructed for a representative set of ocean provinces, between bias and the level of significance associated with the comparison of slopes shows that a bias on the order of ±5-6% generally induces a slope that is significantly different from the reference case, while a threshold on bias values not exceeding 2% largely alleviates this effect. Moreover, the study suggests that a drift larger than 2% per decade on the chl-a series can result in misleading conclusions from a trend analysis. All results have a clear regional dependence that needs to be taken into account in bias-correction and merging efforts. Low chl-a regions, such as the oligotrophic subtropical gyres, appear particularly sensitive to perturbations and require still higher levels of consistency and stability.ARTICLE HISTORY

show abstract

“…In the other recent literatures, SVM has been applied to solve various problems related to remote sensing. For example, monitoring of biophyssical parameters [10][11][12], vegetation classification [13][14][15], road extraction [16][17][18], and landmine detection [19].…”

Section: Introductionmentioning

confidence: 99%

Support vector machine as a binary classifier for automated object detection in remotely sensed data

Wardaya

2014

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data

Cited by 75 publications

References 28 publications

A Review of Kernel Methods in Remote Sensing Data Analysis

A Review of Kernel Methods in Remote Sensing Data Analysis

Impact of inter-mission differences and drifts on chlorophyll-atrend estimates

Support vector machine as a binary classifier for automated object detection in remotely sensed data

Contact Info

Product

Resources

About