MHFNet: An Improved HGR Multimodal Network for Informative Correlation Fusion in Remote Sensing Image Classification

Abstract

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.

Keywords

• Data sparsity
• HGR maximal correlation
• multimodal fusion
• remote sensing image classification