<p>Hyperspectral and multispectral image fusion plays a pivotal role in boosting the spatial resolution of hyperspectral data while simultaneously expanding their applicability across downstream tasks, ranging from precision agriculture and environmental monitoring to urban planning and disaster assessment. Over the past few years, deep learning-based methods have achieved remarkable progress, especially in capturing both global contextual information and fine-grained features from hyperspectral imagery. Some of these models attain impressive quantitative accuracy. However, they often neglect the computational footprint, thereby limiting their deployability on resource-constrained platforms. Moreover, few studies have thoroughly investigated how to guarantee the quality and generalizability of refined feature extraction when confronted with ground objects that exhibit variations in scale and shape. To further enrich and complement the existing body of work, this paper introduces a Hyperspectral-Multispectral Fusion Network, termed AMSGL-Net, which simultaneously pursues efficient global feature extraction and robust, scale-aware representation of complex ground objects. AMSGL-Net contains two important components. First, the Edge Feature Enhancement Module (EFEM) leverages spatial attention mechanism to extract sharp, semantically rich edge cues of large-scale objects, which balances fusion quality against computational efficiency. Second, the Adaptive Multi-Scale Feature Extraction Module (AMSFEM) dynamically adjusts its receptive fields, enabling the network to capture subtle textural details for small instances with different scales, while preserving structural integrity for large ones. We conducted experiments on three public datasets and compared with state-of-the-art methods to demonstrate the effectiveness of AMSGL-Net.</p>