Agricultural vegetation classification with SVM and polarimetric SAR data

Sandrine Daniel; Sophie Allain-Bailhache; Sébastien Angelliaume; Pascale Dubois-Fernandez; Eric Pottier

doi:10.1117/12.865078

22 October 2010 Agricultural vegetation classification with SVM and polarimetric SAR data

Sandrine Daniel, Sophie Allain-Bailhache, Sébastien Angelliaume, Pascale Dubois-Fernandez, Eric Pottier

Proceedings Volume 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII; 78240L (2010) https://doi.org/10.1117/12.865078
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

Polarimetric SAR data at L-band are known to be particularly well adapted for estimating moisture content and roughness. However, many agricultural fields are generally covered by a short vegetation layer that hampers this analysis. In fact, many applications of surface parameter retrieval methods using polarimetric SAR data over agricultural sites revealed that parameters are underestimated over most of the fields covered by short vegetation (e.g. grass, clovers, winter wheat). This bias is due to the electromagnetic contribution of the vegetation which significantly modifies the polarimetric response. An identification of different kind of vegetation is necessary in order to determine the feasibility to estimate soil moisture. The AgriSAR campaign, Agricultural Bio-/Geophysical Retrievals from Frequent Repeat SAR and Optical Imaging, was conducted for ESA in 2006 in order to study the agricultural vegetation. The multi-temporal datasets were acquired with the DLR's E-SAR sensor in Görmin (Germany). From this campaign, many ground measurements were obtained: Leaf Area Index (LAI), wet and dry biomass and soil moisture. Thus, using all information, eight agricultural vegetation classes could be characterized independently of soil moisture. This paper presents this identification necessary to elaborate an original mapping technique allowing localizing agricultural fields having a vegetation layer. A classification based on the support vector machine (SVM) and on the analysis of polarimetric parameter behavior is developed using multi-temporal images over fields covered by vegetation. The obtained vegetation maps allow the analysis of the temporal evolution of plants. This classification has high product and user accuracy which are presented. The technique is shown to perform well over the AgriSAR dataset.

Citation Download Citation

Sandrine Daniel, Sophie Allain-Bailhache, Sébastien Angelliaume, Pascale Dubois-Fernandez, and Eric Pottier "Agricultural vegetation classification with SVM and polarimetric SAR data", Proc. SPIE 7824, Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, 78240L (22 October 2010); https://doi.org/10.1117/12.865078

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