Classification and Monitoring of Reed Belts Using Dual-Polarimetric TerraSAR-X Time Series

Abstract

Synthetic aperture radar polarimetry (PolSAR) and polarimetric decomposition techniques have proven to be useful tools for wetland mapping. In this study we classify reed belts and monitor their phenological changes at a natural lake in northeastern Germany using dual-co-polarized (HH, VV) TerraSAR-X time series. The time series comprises 19 images, acquired between August 2014 and May 2015, in ascending and descending orbit. We calculated different polarimetric indices using the HH and VV intensities, the dual-polarimetric coherency matrix including dominant and mean alpha scattering angles, and entropy and anisotropy (normalized eigenvalue difference) as well as combinations of entropy and anisotropy for the analysis of the scattering scenarios. The image classifications were performed with the random forest classifier and validated with high-resolution digital orthophotos. The time series analysis of the reed belts revealed significant seasonal changes for the double-bounce-sensitive parameters (intensity ratio HH/VV and intensity difference HH-VV, the co-polarimetric coherence phase and the dominant and mean alpha scattering angles) and in the dual-polarimetric coherence (amplitude), anisotropy, entropy, and anisotropy-entropy combinations; whereas in summer dense leaves cause volume scattering, in winter, after leaves have fallen, the reed stems cause predominately double-bounce scattering. Our study showed that the five most important parameters for the classification of reed are the intensity difference HH-VV, the mean alpha scattering angle, intensity ratio HH/VV, and the coherence (phase). Due to the better separation of reed and other vegetation (deciduous forest, coniferous forest, meadow), winter acquisitions are preferred for the mapping of reed. Multi-temporal stacks of winter images performed better than summer ones. The combination of ascending and descending images also improved the result as it reduces the influence of the sensor look direction. However, in this study, only an accuracy of~50% correct classified reed areas was reached. Whereas the shorelines with reed areas (>10 m broad) could be detected correctly, the actual reed areas were significantly overestimated. The main source of error is probably the challenging data geocoding causing geolocation inaccuracies, which need to be solved in future studies.

Scattering Characteristics of X-, C- and L-Band PolSAR Data Examined for the Tundra Environment of the Tuktoyaktuk Peninsula, Canada

“…As well, the potential for land cover classification should be addressed, e.g., via the Random-Forest approach that was shown to provide an interesting classification framework also for PolSAR data [16,51]. In this context, upcoming studies should further acknowledge if the Random-Forest approach is appropriate and essential for a successful PolSAR classification.…”

Two Component Decomposition of Dual Polarimetric HH/VV SAR Data: Case Study for the Tundra Environment of the Mackenzie Delta Region, Canada

“…The linear Pearson Correlation Coefficient (R), the squared linear Pearson Correlation Coefficient (R 2 ), and the Spearman's Rank Correlation Coefficient (ρ) were processed for all of the features for each land cover class of each test site using the reference data. The coefficient R is defined as the ratio between the covariance (Cov) of two variables (i,j) and the product of the individual standard deviations of these two variables (SD) (16) and (17). Similar, ρ is defined as the ratio between the covariance (Cov) of two ranked variables (RG i , RG j ) and the product of the individual standard deviations of these two ranked variables (σ RG i σ RG j ) (18).…”

K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series

“…The success of launching Earth Observation satellites has provided a powerful tool to map Earth's surface and acquire information about targets on the ground. Change detection (CD) has become an important application of remote sensing images [1], such as environmental monitoring [2][3][4], the observation of natural disasters [5], risk management [6], and the change analysis of human activity [7,8]. Compared with optical sensors, synthetic aperture radar (SAR) sensors have many advantages.…”