Henning Skriver

CFAR edge detector for polarimetric SAR images

Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data.

Crop Classification Using Short-Revisit Multitemporal SAR Data

Classification of crops and other land cover types is an important application of both optical/infrared and SAR satellite data. It is already an import application of present satellite systems, as it will be for planned missions, such as the Sentinels. An airborne SAR data set with a short revisit time acquired by the German ESAR system during the ESA-campaign, AgriSAR 2006, has been used to assess the performance of different polarization modes for crop classification. Both C-and L-band SAR data were acquired over the Demmin agricultural test site in North Eastern Germany on a weekly basis during the growing season. Single-and dual-polarization, and fully polarimetric data have been used in the analysis (fully polarimetric data were only available at L-band). The main results of the analysis are, that multitemporal acquisitions are very important for single-and dual-polarization modes, and that cross-polarized backscatter produces the best results, with errors down to 3%-6% at the two frequencies. There is a trade-off between the polarimetric information and the multitemporal information, where the best overall results are obtained using the multitemporal information. If only a few acquisitions are available, the polarimetric mode may perform better than the single-and dual polarization modes.

Impact of Reducing Polarimetric SAR Input on the Uncertainty of Crop Classifications Based on the Random Forests Algorithm

Although the use of multidate polarimetric synthetic aperture radar (SAR) data for highly accurate land cover classification has been acknowledged in the literature, the high dimensionality of the data set remains a major issue. This study presents two different strategies to reduce the number of features in multidate SAR data sets: an accuracy-oriented reduction and an efficiency-oriented reduction. For both strategies, the effect of feature reduction on the quality of the land cover map is assessed. The analyzed data set consists of 20 polarimetric features derived from L-band (1.25 GHz) SAR acquired by the Danish EMISAR on four dates within the period April to July in 1998. The predictive capacity of each feature is analyzed by the importance score generated by random forests (RF). Results show that according to the variation in importance score over time, a distinction can be made between general and specific features for crop classification. Based on the importance ranking, features are gradually removed from the single-date data sets in order to construct several multidate data sets with decreasing dimensionality. In the accuracy-oriented and efficiency-oriented reduction, the input is limited to eight and three features per acquisition, respectively. On the reduced input, a multidate model is built using the RF algorithm. Results indicate a decline in the classification uncertainty when feature reduction is performed.

Crop Classification by Multitemporal C- and L-Band Single- and Dual-Polarization and Fully Polarimetric SAR

Classification of crops and other land cover types is an important application of both optical/infrared and synthetic aperture radar (SAR) satellite data. It is already an import application of present satellite systems, as it will be for planned missions, such as the Sentinels. A multitemporal data set from the Danish airborne polarimetric EMISAR has been used to assess the performance of different polarization modes for crop classification. Both C- and L-band SAR data were acquired simultaneously over the Foulum agricultural test site in Denmark on a monthly basis during the growing season. Single- and dual-polarization and fully polarimetric data have been used in the analysis. The best result for a single-frequency system was a 20%-22% classification error, and the results for C-band and for L-band were very similar. The best result was obtained at C-band using the VVXP polarization combination (the dual-polarization mode, where the VV-channel and the cross-polarized channel have been combined) and at L-band using the fully polarimetric mode with the Hoekman and Vissers classifier. The best result for the combination of the C- and L-bands was 16%. In this case also, the VVXP polarization combination performed best. There is a tradeoff between the polarimetric information and the multitemporal information, where the best overall results are obtained using the multitemporal information.

Optimization of Soil Hydraulic Model Parameters Using Synthetic Aperture Radar Data: An Integrated Multidisciplinary Approach

It is widely recognized that synthetic aperture radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such as, for example, hydraulic conductivity values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing and land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure based on the extended Kalman filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.

Evaluation of the Wishart test statistics for polarimetric SAR data

A test statistic for equality of two covariance matrices following the complex Wishart distribution has previously been used in new algorithms for change detection, edge detection and segmentation in polarimetric SAR images. Previously, the results for change detection and edge detection have been quantitatively evaluated. This paper deals with the evaluation of segmentation. A segmentation performance measure originally developed for single-channel SAR images has been extended to polarimetric SAR images, and used to evaluate segmentation for a merge-using-moment algorithm for polarimetric SAR data.

Comparison between Multitemporal and Polarimetric SAR Data for Land Cover Classification

The investigation focuses on the determination of the land cover type using SAR data, including single polarisation, dual polarisation and fully polarimetric data, at L-band. The analysed data set was acquired during the AgriSAR 2006 campaign by the airborne ESAR system over the Gormin agricultural site (Northeast Germany). The multitemporal acquisitions significantly improve the classification results for single and dual polarization configurations. The best results for the single and dual polarization configurations are better than for the polarimetric mode. Overall, the cross-polarisation configuration provides the best results.

Signatures of polarimetric parameters and their implications on land cover classification

Knowledge-based or rule-based classification schemes provide robust classification of normally a few major classes. In order to determine optimum polarimetric parameters for such classification schemes, a study has been performed, where the separability between different sets of major classes using many different polarimetric parameters has been investigated using airborne,C- and L-band polarimetric SAR data.