# Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach

arXiv: https://arxiv.org/abs/2303.12533 · HTML: https://mlsanity.com/paper/2303.12533/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 92 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Self-labelling via simultaneous clustering and representation learning](https://mlsanity.com/paper/1911.05371/) (2019)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/1901.10738/) (2019)
- [Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding](https://mlsanity.com/paper/2106.00750/) (2021)
- [Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks](https://mlsanity.com/paper/2107.07933/) (2021)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series](https://mlsanity.com/paper/1811.10166/) (2018)
- [Self-attention for raw optical Satellite Time Series Classification](https://mlsanity.com/paper/1910.10536/) (2019)
- [Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention](https://mlsanity.com/paper/1911.07757/) (2019)
- [ViTs for SITS: Vision Transformers for Satellite Image Time Series](https://mlsanity.com/paper/2301.04944/) (2023)
- [Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series](https://mlsanity.com/paper/2007.00586/) (2020)
- [Multivariate Time Series Classification with WEASEL+MUSE](https://mlsanity.com/paper/1711.11343/) (2017)
- [Unsupervised Layered Image Decomposition into Object Prototypes](https://mlsanity.com/paper/2104.14575/) (2021)
- [Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification](https://mlsanity.com/paper/2002.10061/) (2020)
- [Fast and Accurate Time Series Classification with WEASEL](https://mlsanity.com/paper/1701.07681/) (2017)
- [BreizhCrops: A Time Series Dataset for Crop Type Mapping](https://mlsanity.com/paper/1905.11893/) (2019)
- [Deep Transformation-Invariant Clustering](https://mlsanity.com/paper/2006.11132/) (2020)
- [Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition](https://mlsanity.com/paper/1512.06747/) (2015)
- [Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks](https://mlsanity.com/paper/1704.04055/) (2017)
- [End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping](https://mlsanity.com/paper/1901.10681/) (2019)
- [Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding](https://mlsanity.com/paper/2203.09175/) (2022)
- [Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/1811.02471/) (2018)
- [DeepNAG: Deep Non-Adversarial Gesture Generation](https://mlsanity.com/paper/2011.09149/) (2020)
- [Representing Shape Collections with Alignment-Aware Linear Models](https://mlsanity.com/paper/2109.01605/) (2021)
- [A Model You Can Hear: Audio Identification with Playable Prototypes](https://mlsanity.com/paper/2208.03311/) (2022)

## Built on by (most cited first, 8 of 8)

- [Satellite Image Time Series Semantic Change Detection: Novel Architecture and Analysis of Domain Shift](https://mlsanity.com/paper/2407.07616/) (2024)
- [FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mapping](https://mlsanity.com/paper/2506.07080/) (2025)
- [SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping](https://mlsanity.com/paper/2608.09497/) (2026)
- [Detecting Looted Archaeological Sites from Satellite Image Time Series](https://mlsanity.com/paper/2409.09432/) (2024)
- [CoDEx: Combining Domain Expertise for Spatial Generalization in Satellite Image Analysis](https://mlsanity.com/paper/2504.19737/) (2025)
- [T3S: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series](https://mlsanity.com/paper/2506.12885/) (2025)
- [SITS-DECO: A Generative Decoder Is All You Need For Multitask Satellite Image Time Series Modelling](https://mlsanity.com/paper/2510.21813/) (2025)
- [TIDES: Implicit Time-Awareness in Selective State Space Models](https://mlsanity.com/paper/2605.09742/) (2026)

## Abstract (arXiv)

Improvements in Earth observation by satellites allow for imagery of ever higher temporal and spatial resolution. Leveraging this data for agricultural monitoring is key for addressing environmental and economic challenges. Current methods for crop segmentation using temporal data either rely on annotated data or are heavily engineered to compensate the lack of supervision. In this paper, we present and compare datasets and methods for both supervised and unsupervised pixel-wise segmentation of satellite image time series (SITS). We also introduce an approach to add invariance to spectral deformations and temporal shifts to classical prototype-based methods such as K-means and Nearest Centroid Classifier (NCC). We study different levels of supervision and show this simple and highly interpretable method achieves the best performance in the low data regime and significantly improves the state of the art for unsupervised classification of agricultural time series on four recent SITS datasets.