Summary of Low-resource Crop Classification From Multi-spectral Time Series Using Lossless Compressors, by Wei Cheng et al.
Low-Resource Crop Classification from Multi-Spectral Time Series Using Lossless Compressors
by Wei Cheng, Hongrui Ye, Xiao Wen, Jiachen Zhang, Jiping Xu, Feifan Zhang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed non-training framework for crop classification uses a Symbolic Representation Module to convert multispectral temporal data into symbolic representations. These symbolic embeddings are then compared using the Multi-scale Normalised Compression Distance (MNCD) and classified using a k-nearest-neighbor classifier (kNN). The framework outperformed 7 advanced deep learning models in benchmark datasets, and over half of these models in few-shot settings with sparse crop labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to classify crops uses special codes to turn data into simple symbols. These symbols are then compared to find the best match for each type of crop. This method doesn’t need a lot of training and works well even with limited labeled samples. It’s better than many deep learning models and can be used in real-world applications. |
Keywords
» Artificial intelligence » Classification » Deep learning » Few shot » Nearest neighbor
