# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 3 Results  
### Data Description  
(1) Augsburg dataset: This dataset describes the land cover around Augsburg, Germany, and consists of remotely sensed images acquired by two different remote sensing instruments, namely, satellite-based HSI and polarized synthetic-aperture radar (PolSAR) images. The HSI data were acquired using the HySpex sensor, while the PolSAR images were obtained from the sentinel-1 sensor. The spatial resolution of all images in the Augsburg dataset is ground sampling distance (GSD) to a uniform resolution of 30 m to facilitate multimodal data fusion. Specifically, the Augsburg dataset has a spatial size of 332 \(\times\) 485 pixels, where the HSI consists of 180 spectral bands in the range of 0.4-2.5 \(\upmu\)m and the SAR consists of four polarization decomposition features (The elements VV-VH, which correspond to the off-diagonal components of the polarized synthetic aperture radar (SAR) covariance matrix, represent the real and imaginary parts respectively). Ground truth maps for the Augsburg dataset were provided by [PERSON] et al. [36] and hand-labeled by experts with extensive knowledge of fieldwork and photo interpretation.  
The Augsburg dataset is publicly and freely available from [PERSON] et al. [37]. The data can be downloaded from the website: [[https://mediatum.ub.tum.de/1657312](https://mediatum.ub.tum.de/1657312)]([https://mediatum.ub.tum.de/1657312](https://mediatum.ub.tum.de/1657312)) (accessed on 30 January 2024). Table 1 provides information on the categories involved in the scenarios as well as the sizes of both training and test sets.  
(2) Berlin dataset: The dataset provides a comprehensive description of Berlin city and its rural periphery. The first data are synthesized simulated EnMAP data based on HyMap HS data. The second data are from the Sentinel-1 dual polarization (VV-VH) single look complex (SLC) product, downloaded from the European Space Agency. Specifically, the HSI spatial resolution is GSD to 30 m, and the HyMap hyperspectral sensor records wavelengths ranging from 400 to 2500 nm, 244 spectral bands, and a spatial size of 797 \(\times\) 220 pixels. The spatial resolution of the SAR image is GSD to 13.89 m, the spatial size is 1723 \(\times\) 476 pixels, and four dual-polarized bands (VV-VH) are used. The SAR data are fed into a pre-processing pipeline based on the SNAP toolbox downloaded from ESA, including orbit correction, radiometric correction, and speckle reduction. The algorithm of nearest neighbor interpolation is used on the HSIs to match the spatial size of the SAR images. Additionally, [PERSON] et al. [36] provide ground truth maps and expert-labeled sample data. Table 2 lists the categories of the dataset, the training data, and the test  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class No** & **Class Name** & **Training Set** & **Testing Set** \\ \hline C1 & Forest & 2026 & 11,481 \\ \hline C2 & Residential Area & 4549 & 25,780 \\ \hline C3 & Industrial Area & 578 & 3273 \\ \hline C4 & Low Plants & 4029 & 22,828 \\ \hline C5 & Allotment & 86 & 489 \\ \hline C6 & Commercial Area & 247 & 1398 \\ \hline C7 & Water & 230 & 1300 \\ \hline  & Total & 11,745 & 66,549 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Describes the sizes of training and test sets for the Augsburg dataset.  
data. The Berlin dataset is publicly and freely available from [PERSON]. [PERSON] et al. (2018). The data can be downloaded from the following website: [[http://doi.org/10.5880/enmap.2016.002](http://doi.org/10.5880/enmap.2016.002)]([http://doi.org/10.5880/enmap.2016.002](http://doi.org/10.5880/enmap.2016.002)) (accessed on 25 February 2024).