Pol-InSAR-Island is the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) benchmark dataset for land cover classification. The strong scientific interest and the accompanying rapid development of machine learning, in particular deep learning, has led to a significant improvement in automatic image interpretation in recent years. Research generally focuses on classification or segmentation of optical images, but there are already several successful approaches that apply deep learning techniques to the analysis of PolSAR or Pol-InSAR images. While the success of learning-based methods for the analysis of optical images has been strongly driven by public benchmark datasets such as ImageNet and Cityscapes, which contain a large number of annotated training and test data, comparable datasets for the PolSAR and especially the Pol-InSAR domain are almost non-existent. To fill this gap, this work presents a new multi-frequency Pol-InSAR benchmark dataset for training and testing learning-based methods. The dataset contains Pol-InSAR data acquired in S- and L-band by DLR’s airborne F-SAR system over the East Frisian island Baltrum. To allow interferometric analysis a repeat-pass configuration with a time offset of several minutes and a vertical baseline of 40 m is used. The image data are given as geocoded 6 × 6 coherency matrices on a 1 m × 1 m grid and is labeled by 12 different land cover classes. The Pol-InSAR-Island dataset is intended to improve the development of new learning-based approaches for multi-frequency Pol-InSAR classification. To ensure the comparability of various approaches, a defined division of the data into training and testing sections is given.

For more information, refer to the corresponding research article: https://doi.org/10.1016/j.ophoto.2023.100047

Pol-InSAR-Island - A Benchmark Dataset for Multi-frequency Pol-InSAR Data Land Cover Classification (Version 2) is the updated version of the dataset. The PolSAR as well as the label images remain unchanged, but additional files containing the corresponding incidence angle and the vertical wavenumbers are added.

Pol-InSAR-Island dataset:

This folder contains multi-frequency Pol-InSAR data acquired by the F-SAR system of the German Aerospace Center (DLR) over Baltrum and corresponding land cover labels.

Data structure: - data - FP1 # Flight path 1 - L # Frequency band - T6 # Pol-InSAR data - incidence.bin # Incidence angle [rad] - kz_*.bin ' Vertical wavenumber for vv, hv, vh and vv polarization [rad/m] - pauli.bmp # Pauli-RGB image of the master scene - S - ... - FP2 # Flight path 2 - ... - label # Land cover label - FP1 # Flight path 1 - label_train.bin # Geocoded training label - label_test.bin # Geocoded test label - ... - FP2 # Flight path 2 - ...

Data format: The data is provided as flat-binary raster files (.bin) with an accompanying ASCII header file (*.hdr) in ENVI-format. For Pol-InSAR data the real and imaginary components of the diagonal elments and upper triangle elements of the 6 x 6 coherency matrix are stored in seperated files (T11.bin, T12_real.bin, T12_imag.bin,...)

Land cover labels contained in label_train.bin and label_test.bin are encoded as integers using the following mapping:

0 - Unassigned 1 - Tidal flat 2 - Water 3 - Coastal shrub 4 - Dense, high vegetation 5 - White dune 6 - Peat bog 7 - Grey dune 8 - Couch grass 9 - Upper saltmarsh 10 - Lower saltmarsh 11 - Sand 12 - Settlement