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In the realm of urban development, the precise classification and identification of land types are crucial for improving land use efficiency. This article proposes a land recognition and classification method based on data sparsity and improved Soft Hirschfeld-Gebelein-Rényi (Soft-HGR) under multimodal conditions. First, a sparse information processing module is designed to enhance information accuracy and quickly obtain data sample features. Then, to solve the problem of information independence in single mode and lack of fusion in multimodal mode, an improved SoftHGR module is developed. This module incorporates covariance and trace constraints, enhances machine learning efficiency by stabilizing output and addressing dimensionality and variance issues in HGR, and speeds up land classification by cross-fusing multimodal features to deepen the understanding of diverse information interconnections. Based on this, a multimodal MI-SoftHGR fusion network is constructed, which can achieve cross-correlation sharing and collaborative extraction of feature information, thereby realizing accurate remote sensing image recognition and classification under multimodal conditions. Finally, empirical evaluations were conducted on Berlin, Augsburg, and MUUFL datasets, and the proposed method was compared with state-of-the-art algorithms. The results fully validate the efficacy and significant superiority of the proposed method.

This paper investigates remote sensing data recognition and classification with multimodal data fusion. Aiming at the problems of low recognition and classification accuracy and the difficulty in integrating multimodal features in existing methods, a multimodal remote sensing data recognition and classification model based on a heatmap and Hirschfeld–Gebelein–Rényi (HGR) correlation pooling fusion operation is proposed. A novel HGR correlation pooling fusion algorithm is developed by combining a feature fusion method and an HGR maximum correlation algorithm. This method enables the restoration of the original signal without changing the value of transmitted information by performing reverse operations on the sample data. This enhances feature learning for images and improves performance in specific tasks of interpretation by efficiently using multi-modal information with varying degrees of relevance. Ship recognition experiments conducted on the QXS-SROPT dataset demonstrate that the proposed method surpasses existing remote sensing data recognition methods. Furthermore, land cover classification experiments conducted on the Houston 2013 and MUUFL datasets confirm the generalizability of the proposed method. The experimental results fully validate the effectiveness and significant superiority of the proposed method in the recognition and classification of multimodal remote sensing data.