# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 1 Introduction  
Hyperspectral images (HSIs), multispectral images (MSIs), light detection and ranging (LiDAR), and synthetic-aperture radar (SAR) images are widely employed in remote sensing to capture distinct characteristics of the same ground features. HSIs have rich spectral characteristics and can distinguish ground objects with similar textures and different spectra. MSIs effectively capture and represent the color, brightness, and distinctive features of various ground objects, thereby exhibiting remarkable recognition capabilities in urban environments encompassing streets, buildings, water bodies, soil compositions, and vegetation types. SAR images primarily capture two types of ground target characteristics: structural attributes (such as texture and geometry) and electromagnetic scattering properties (including dielectric and polarization features). In theory, the utilization of complementary information from multimodal remote sensing images has the potential to enhance feature classification accuracy and compensate for limitations associated with single-channel imagery. By merging data from various modalities, a more comprehensive feature representation can be constructed, enabling the acquisition of detailed information [1]. This approach to interpreting remote sensing images is highly beneficial for a range of research and application areas, including land cover classification [2], target detection, semantic segmentation [3], environmental monitoring, mineral exploration [4], urban planning, medical diagnostics [4], precision agriculture, disaster response [5], and foodsecurity [6]. Notably, land cover classification has emerged as a particularly prominent area of application. Therefore, the utilization of multimodal remote sensing image classification holds significant research value. Passive remote sensing encompasses various types of images, such as visible (VIS) images, MSIs, HSIs, and others. Particularly, MSIs have the capability to capture spectral information across the visible to infrared spectra. To achieve satisfactory results, spatial-spectral classification models are employed [7]. However, the quality of decoded images is affected by the low resolution and sensitivity to weather conditions in HSIs. On the other hand, active remote sensing involves SAR and LiDAR. Polarized SAR images capture scattered echoes using horizontal and vertical polarization from surface cover. Additionally, SAR offers the advantage of all-weather observation for severe weather conditions. Nevertheless, SAR images are susceptible to significant scattering noise phenomena. The limitations of relying solely on SAR images for classification often lead to unsatisfactory results. Hence, integrating HSI and SAR data can enhance the accuracy of classification by leveraging their respective strengths [8]. [PERSON] et al. [9] proposed a CNN-based classification method for multimodal remote sensing data. The proposed model uses HSI and SAR fused remote sensing data and uses complementary information of spatial and spectral features to improve the classification accuracy. The experimental results show that compared with the classification method using single SAR remote sensing image, the classification accuracy using multimodal remote sensing data is higher, which shows its potential application in remote sensing interpretation. Data fusion enhances the dependability of image analysis by emphasizing valuable thematic details [10]. The synergistic utilization of data from multiple sources is usually achieved through pixel-by-pixel superimposition, which inevitably results in information redundancy [11]. Early efforts to categorize remotely sensed data mainly used a single type of remotely sensed data. For example, hyperspectral image classification uses hyperspectral data. Current classification techniques for HSIs can produce satisfactory results, but there is a possibility of misclassifications, particularly in urban areas where roads and building roots may have similar materials and spectral profiles.