  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.
