Combination of Time Series of L-, C-, and X-Band SAR Images for Land Cover and Crop Classification

The availability of new Earth observation satellites operating radar sensors at different frequencies enables the combination of multiple dimensions of the data (time, frequency, polarimetry, and interferometry) in many applications. Image classification is expected to benefit from the diversity of observation. This work illustrates classification experiments carried out with series of images acquired by ALOS-2 PALSAR (L-band), Sentinel-1 (C-band), and TanDEM-X (X-band) in two application domains: 1) land cover classification and 2) crop-type mapping. Their usage, both separately and in combination, serves to identify the complementarity of information. In this work, we propose a new color representation of the pairwise class separability in the case of using three frequency bands, which help identify which bands (or combinations of them) provide the best performance. Results in terms of accuracy scores (overall and class-specific) show that the use of the three frequency bands always outperforms the individual hands and their pairs. In addition, for both land classification and crop-type mapping the accuracy of using coherence time series is lower than the one obtained with the intensity time series, but there is complementarity in terms of sensitivity when both coherence and intensity time series are used together. The classes which are most benefited at each particular case of study have been identified. Finally, a partial tradeoff has been found between the use of multiple frequency bands and the length of the available time series.