6th International Workshop on Retrieval of Bio- & Geo-physical Parameters from SAR Data for Land Applications

Physical interpretation of the Polarimetric SAR changes observed over agricultural time series

18 Nov 2015, 14:00

20m

Harwell, UK

Agriculture IV - Agriculture

Speaker

Dr Alberto Alonso-Gonzalez (DLR)

Description

In recent years, the presence of space-borne SAR systems has empowered the construction of dense time series datasets, containing SAR images of the same scene at different time instants. The importance of these datasets lies in that they contain information not only about the scene itself but also about its temporal evolution. Moreover, this is a growing trend which is expected to continue in the near future, since most of the current and planned SAR missions are focused on the construction of time series, as the ESA Sentinel-1 or the DLR TerraSAR-X, TanDEM-X and the future TanDEM-L. This work is focused on the analysis of the temporal evolution of different agricultural fields and crop types by means of polarimetric time series datasets. The Binary Partition Tree (BPT) [1] is employed in order to improve the analysis of PolSAR time series. The BPT may be considered as a hierarchical region-based and multi-scale data representation. This technique has already been extended to process PolSAR [2][3] and hyperspectral data [4], demonstrating its ability to detect the homogeneous regions of the scene while also preserving the contours and the small details of the data. The BPT has also been extended to process SAR time series, as described in [5]. In the context of agricultural monitoring, the BPT is particularly useful, as it allows a precise characterization of the polarimetric signature of individual fields and crop types and its temporal evolution. In [6] a polarimetric change analysis has been proposed in order to give information not only about the amount of change, but also about the type of change. It decomposes the observed changes in the polarimetric space, giving information of the changes for different polarization states, being able to separate different behaviors. A more detailed analysis of these time series datasets is proposed, with special attention to the characterization of the temporal evolution of the scene. The polarimetric change analysis technique described in [6] will be applied. In this work, the focus will be put in the physical interpretation of these changes, trying to link some physical properties of the target with the observed changes. Due to the multidimensional nature of the polarimetric data, the technique is able to detect different changes at distinct polarization states. This will help in the understanding of the physical changes and their relation with the polarimetric target response. The method will be applied to the AgriSAR 2006 campaign and also the Wallerfing 2014 campaign, consisting of a set of E-SAR and F-SAR acquisitions over agricultural fields and an extensive collection of in-situ ground measurements. Moreover, in these datasets different types of data are available for exploration. This includes multi-frequency acquisitions at X-, C- and L-band, and interferometric data with different spatial and temporal baselines that cover different stages of the phenological cycle from April to August. All this information will be exploited in order to explore and interpret the polarimetric changes observed among these data. Additionally, the ground truth information and measurements will be used in order to analyse, interpret and verify the obtained results. Particular attention will be taken to the final land applications that may take profit of the proposed processing as, for instance, crop identification and monitoring, change detection and characterization or bio/geophysical parameter retrieval. [1] Salembier, P.; Garrido, L., “Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval,” IEEE TIP, vol. 9, no. 4, pp. 561–576, 2000. [2] Alonso-Gonzalez, A.; Lopez-Martinez, C.; Salembier, P., “Filtering and segmentation of polarimetric SAR images with Binary Partition Trees,” in Proc. IEEE IGARSS, 2010, pp. 4043–4046. [3] Alonso-Gonzalez, A.; Lopez-Martinez, C.; Salembier, P., “Filtering and segmentation of Polarimetric SAR data based on Binary Partition Trees,” IEEE TGRS, vol. 50, no. 2, pp. 593 –605, 2012. [4] Alonso-Gonzalez, A.; Valero, S.; Chanussot, J.; Lopez-Martinez, C.; Salembier, P., “Processing Multidimensional SAR and Hyperspectral Images With Binary Partition Tree,” Proceedings of the IEEE, vol.101, no.3, pp.723,747, 2013. [5] Alonso-Gonzalez, A.; Lopez-Martinez, C.; Salembier, P., “PolSAR Time Series Processing With Binary Partition Trees,” IEEE TGRS, vol.52, no.6, pp.3553,3567, 2014. [6] Alonso-Gonzalez, A.; Jagdhuber, T.; Hajnsek, I., “Exploitation of agricultural Polarimetric SAR time series with Binary Partition Trees,” ESA PolInSAR, 2015.