Few-shot object detection (FSOD) in remote sensing images (RSIs) faces challenges such as data scarcity, difficulty in detecting small objects, and underutilization of frequency-domain information. Existing methods often rely on spatial-domain features, neglecting the complementary insights from low- and high-frequency characteristics. Additionally, their performance in detecting small objects is hindered by inadequate feature extraction in cluttered backgrounds. To tackle these problems, we propose a novel detection framework of Spatial-Frequency Interaction and Distribution Matching (SFIDM), which significantly enhances FSOD performance in RSIs. SFIDM focuses on rapid adaptation to target datasets and efficient fine-tuning with limited data. First, to improve feature representation, we introduce the Spatial-Frequency Interaction (SFI) module, which leverages the complementarity between low-frequency and high-frequency information. By decomposing input images into their frequency components, the SFI module extracts features critical for classification and precise localization, enabling the framework to capture fine details essential for detecting small objects. Secondly, to resolve the limitations of traditional label assignment strategies when dealing with small bounding boxes, we construct the Distribution Matching (DM) module, which models bounding boxes as 2D Gaussian distributions. This allows for the accurate detection of subtle offsets and overlapping or non-overlapping small objects. Moreover, to leverage the learned base-class information for improved performance on novel class detection, we employ a feature reweighting module, which adaptively fuses features extracted from the backbone network to generate representations better suited for downstream detection tasks. We conducted extensive experiments on two benchmark FSOD datasets to demonstrate the effectiveness and performance improvements achieved by the proposed SFIDM framework.