真实土地地块在面积和复杂性方面具有高度变异性。现有基于深度学习的模型通常依赖网格划分或简单重采样以保证输入尺寸的一致性，这使得在保留信息的同时难以准确反映真实的土地分布情况。当前研究还缺乏对模型识别过程及数据融合结果的可解释性分析。为解决上述局限，本文提出一种新型多源数据融合方法——不规则地块分类模型（IPCM）。IPCM采用泊松盘采样对样本进行规范化处理，同时保留关键信息。通过梯度加权类别激活映射++（Grad-CAM++）与可解释性提升算法（Explainable Boosting），对IPCM的分类过程及数据融合机制进行可解释性分析。结果表明，该模型在多模态数据中对不同功能类别的关注程度存在差异，并揭示了由于融合过程中信息不匹配导致部分类别融合效果较差的现象；此外，模型通过优化数据融合权重，增强有效信息的贡献，从而缓解多源数据信息严重不匹配带来的负面影响。优化后的IPCM在不规则地块上的测试准确率达到0.892，Kappa系数为0.862。本研究可为高精度土地利用制图及数据融合过程的理解提供重要参考。

Real land parcels exhibit high variability in size and complexity. Existing deep learning-based models often rely on grids or simple resampling to ensure consistent input sizes, which makes it hard to accurately represent real land distribution while preserving information. Current research also lacks the interpretability of model recognition and data fusion. In order to address these limitations, this article introduces the irregular parcel classification model (IPCM), a novel multisource fusion approach. IPCM uses Poisson disk sampling to regularize samples while retaining essential information. IPCM’s interpretability is analyzed by exploring its classification process and data fusion using gradient-weighted class activation mapping++ (Grad-CAM++) and Explainable Boosting. The results highlight the model’s attention to different functional categories in multimodal data and reveal the phenomenon of poorer fusion results for certain categories due to information mismatch during the fusion process; furthermore, the model optimizes data fusion weight to enhance the correct information to mitigate the negative impact when multisource data information has excessive mismatch. Optimized IPCM achieves 0.892 test accuracy and 0.862 Kappa on irregular parcels. This research can serve as an important reference for high-precision land use mapping and understanding the data fusion process.