# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
## 1 Introduction
Hence, it becomes challenging to differentiate these feature types using spectral data alone. However, the utilization of LiDAR-based digital surface model (DSM) data deserves appreciation as it enables the determination of the spatial location of these two feature types [12]. For example, in the study of [PERSON] et al. [13], the height information of ground objects can be obtained using LiDAR images, and the height of two objects can be determined, so that they can be easily distinguished. If the data from these two modalities can be effectively combined, they can compensate each other, and thus, improve the classification accuracy. The current shallow models for multimodal remote sensing image classification primarily consist of morphological profiles (MPs) and subspace learning, which have demonstrated successful applications in feature fusion and classification tasks pertaining to multimodal remote sensing images. MPs refer to the characteristics or indicators utilized for describing and quantifying the shape, structure, and features of an image in morphological image processing. These features are widely used in various fields of classification, such as remote sensing image classification, detection, and segmentation. Through the extraction and analysis of these feature attributes, the detailed information and salient features of the image target can be obtained. Subspace learning is a machine learning method that aims to discover the latent subspace structure in the data. It works by mapping the data into a low-dimensional subspace in order to extract important features and structures in the data. These methods can be used for dimensionality reduction, feature extraction, data clustering, and other tasks. [PERSON] et al. [14] proposed a graph-based subspace learning approach for the fusion of HSI and LiDAR data, with a specific focus on integrating MPs from both datasets on the manifold to enhance discrimination between different land cover classes and improve overall classification accuracy. [PERSON] et al. [15] suggested an alternative method for land cover classification by jointly extracting attribute profiles (APs), rather than MPs, from HSI and LiDAR data sources. However, it is difficult for the method to handle complex scenes, such as intersecting features and shadows, which may lead to inaccurate classification results. [PERSON] et al. [16] proposed an integrated classifier that integrates HSI and LiDAR data for land cover classification, leveraging both spectral and structural features to enhance discrimination between different classes. The incorporation of these two types of data enables the ensemble classifier to capture diverse aspects of the information, thereby improving overall classification accuracy. [PERSON] et al. [17] proposed a comprehensive core framework for the classification and change detection of multi-temporal and multi-source remote sensing data based on subspace learning. The objective is to identify a discriminant information with minimal preservation, facilitating efficient classification and change detection of RS data with nonlinear relationships. By integrating data from diverse sources and time periods, this framework facilitates a more holistic understanding of underlying processes while enhancing performance in classification and change detection. [PERSON] et al. [18] assessed the similarity of multimodal remote sensing data using both Euclidean distance-based and angular distance-based embeddings. The aforementioned distance measures are applied to the feature space of remote sensing data with the objective of providing a comprehensive understanding of the similarities and dissimilarities among diverse data sources. However, there are noisy images, leading to inaccurate classification results. [PERSON] et al. [19; 20; 21] performed learning and regression via a common subspace on HSI and MSI multimodal data.