## 1 Introduction  
Image fusion is a popular area of research in remote sensing image processing, with the goal of enhancing spatial and temporal resolution, change detection, reliability, display capabilities, and system performance robustness [1]. Remote sensing imagery offers a practical and cost-effective means for good environmental management [2], especially when large areas have to be monitored, or information is needed periodically, by measuring earth features from spectral characteristics. For example, using pansharpening to improve the resolution of remote sensing images can help researchers better identify and study natural phenomena or environmental changes [3]. As remote sensing continues to evolve, the development of advanced DL models will undoubtedly play a pivotal role in overcoming existing challenges and unlocking new possibilities for Earth observation [4]. However, remote sensing sensors cannot capture high-resolution hyperspectral images because of their inherent limitations. To be specific, they can obtain PAN and MS images separately [5]. Remote sensing satellite imaging systems grapple with the challenge of concurrently acquiring images that possess high-resolution multispectral (HRMS) images. While enhancing the hardware may address this issue, it is not a viable solution given the stringent limitations imposed by the SNR of satellite products [6]. In order to tackle this challenge, researchers have proposed the pansharpening technique. In recent years, Convolutional Neural Networks (CNNs) have become a fundamental framework in the field of computer vision, playing a crucial role in various deep-learning applications. Nevertheless, recent studies indicate that CNNs often establish short-range dependencies while overlooking global contextual information. Fortunately, Transformer models are capable of overcoming this limitation and have been broadly utilized in natural language processing (NLP) since their introduction [7]. Subsequently, their application has expanded to computer vision (CV) tasks in recent years, including pansharpening, where they demonstrate state-of-the-art (SOTA) performance. In recent studies, some researchers have tried to leverage the strengths of Transformers for pansharpening tasks. For example, [PERSON] et al. [8] presented a network by using the transformer structure [9]. By taking advantage of transformers in computer vision, this paper proposes a dual cross-modality network for pansharpening. Initially, features specific to each modality are extracted from PAN and MS images utilizing Self-Attention Blocks (SABs), and these features are subsequently passed to the Cross-Modality Feature Fusion Module (CMFFM). CMFFM can fuse both spatial and spectral features, effectively integrating relevant and complementary information from the PAN and MS images. Finally, the fused features are sent to the reconstruction module to obtain a pansharpened image. The main contributions of our work can be summarized in three key aspects:  
1. We propose two feature extraction modules: the PAN Feature Extraction Module (PFEM) and the MS Feature Extraction Module (MFEM). These modules utilize multiple Self-Attention Blocks (SABs) to extract modality-specific features from PAN and MS images. 2. We present CMFFM that can efficiently combine the cross-modality features of PAN and MS images. CMFFM can improve the incorporation of modality-specific information, improving pansharpening performance. 3. We introduce a network called DCMFN which integrates the modality-specific features. By incorporating details from the fused features, the network improves the complete acquisition of modality-specific information, leading to improved reconstruction results. The structure of the subsequent sections is presented below. Section 2 provides an overview of the related work, while Section 3 presents a detailed explanation of the proposed DCMFN.