Deep learning has significantly improved the accuracy of crop classification using multispectral temporal data. However, these models have complex structures with numerous parameters, requiring large amounts of data and costly training. In low-resource situations with fewer labeled samples, deep learning models perform poorly due to insufficient data. Conversely, compressors are data-type agnostic, and non-parametric methods do not bring underlying assumptions. Inspired by this insight, we propose a non-training alternative to deep learning models, aiming to address these situations. Specifically, the Symbolic Representation Module is proposed to convert the reflectivity into symbolic representations. The symbolic representations are then cross-transformed in both the channel and time dimensions to generate symbolic embeddings. Next, the Multi-scale Normalised Compression Distance (MNCD) is designed to measure the correlation between any two symbolic embeddings. Finally, based on the MNCDs, high quality crop classification can be achieved using only a k-nearest-neighbor classifier kNN. The entire framework is ready-to-use and lightweight. Without any training, it outperformed, on average, 7 advanced deep learning models trained at scale on three benchmark datasets. It also outperforms more than half of these models in the few-shot setting with sparse crop labels. Therefore, the high performance and robustness of our non-training framework makes it truly applicable to real-world crop mapping. Codes are available at: https://github.com/qinfengsama/Compressor-Based-Crop-Mapping.

翻译：深度学习显著提升了利用多光谱时序数据进行作物分类的准确性。然而，这些模型结构复杂、参数众多，需要大量数据和昂贵的训练成本。在标注样本较少的低资源场景下，深度学习模型因数据不足而表现不佳。相比之下，压缩器对数据类型无特定要求，且非参数方法不引入潜在假设。受此启发，我们提出了一种无需训练的深度学习模型替代方案，旨在应对此类情况。具体而言，我们提出了符号表示模块，将反射率转换为符号表示。随后，这些符号表示在通道和时间维度上进行交叉变换，以生成符号嵌入。接着，设计了多尺度归一化压缩距离（MNCD）来度量任意两个符号嵌入之间的相关性。最后，基于MNCD，仅需使用k近邻分类器（kNN）即可实现高质量的作物分类。整个框架即用且轻量。在未经任何训练的情况下，该框架在三个基准数据集上平均超越了7个经过大规模训练的先进深度学习模型。在作物标签稀疏的少样本设置中，其性能也优于超过半数的上述模型。因此，我们提出的非训练框架凭借其高性能和鲁棒性，能够真正适用于实际作物制图任务。代码发布于：https://github.com/qinfengsama/Compressor-Based-Crop-Mapping。