FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs
Abstract
:1. Introduction
- supervised anomaly detection, where a model is trained to detect anomalies that are labeled as such. Such an approach is prevalent in medical applications for novelty detection [19]. However, having a labeled anomaly dataset is rare, and such a training paradigm is not robust against unexpected anomalies.
- semi-supervised anomaly detection [20,21]: labels of anomalies and normal instances are still present but in a significant imbalance. In this context, deep autoencoders [22] are used. They are unsupervised deep learning models trained with a reconstruction task. In a semi-supervised context, they are trained only on normal observations. Then, deviating instances are used to fit a reconstruction performance threshold above which anomalies can be separated from the norm. However, despite requiring a much lower amount of anomalies than supervised anomaly detection, there is still a need for such labels. If there is no anomalous label on hand, one must use unsupervised anomaly detection.
- unsupervised anomaly detection [23]: methodologies of this kind train deep autoencoders in the same way, but without knowledge of which data point is normal and which is an anomaly. The distinction between the two classes is entirely made from data and is much harder to find. However, it is much more robust to new unseen kinds of anomalies.
- we use deep convolutional autoencoders to model, without supervision, the expected temporal signature of crops in Sentinel-1 multitemporal images.
- we leverage the reconstruction performance of autoencoders as a class belongingness measure and present an automatic binary thresholding strategy for confidence relabeling using Otsu thresholding [31].
- we combine time series-level and parcel-level analysis to better extract and correct anomalies.
2. The Stakes in Agricultural Ground Truths
2.1. The Value of Ground Truths
2.2. The Difficulties of Building Agricultural Datasets
2.3. The Impacts of Errors in Ground Truth Data
2.4. Ontology of Studied Crop Type Errors
- Each parcel is atomic, because they are not supposed to be dividable into smaller parcels.
- The atomicity of parcels is assured by the homogeneity of the crop type: every part of the parcel contains the same plant. We can then assign to the field this crop type as a class.
3. The FARMSAR Methodology
3.1. SAR Temporal Modeling of Crops, a Study Case of Sector BXII, Sevilla
- A first group, representing 50% of the crops (field-wise), is used for quantitative validation of the methodology. In this group, we filter out any suspicious crop, with a process that we detail in the following sections, only to keep crops with high confidence in the veracity of their labels. We then perform repeated random introduction of label errors ten separate times for accurate statistical and numerical evaluation of the proposed methodology. The reader may also find details of this process in the validation section.
- A second group, representing the other 50% of the crops, illustrates the methodology workflow and is corrected. We extract what FARMSAR classifies as mis-split and mislabeled crops and evaluate the appointed corrections qualitatively using Sentinel-2 imagery.
3.2. Convolutional Autoencoder for SAR Time Series
- The convolutional encoder uses convolutions to extract temporal features from the input time series that are then transformed by a stack of fully-connected layers (FC Layers), with Exponential Linear Unit (ELU) activation functions [40], and projected onto an embedding space of low dimension.
- The decoder consists of a stack of fully-connected layers, combined with ELU activation functions, tasked with reconstructing the original time series, from the embedding space representation, through a mean square error loss function computed between the input time series and the output of the decoder.
3.3. Detection of Mis-Split and Mislabeled Crops
3.3.1. Iterative Training of Class-Expert CAEs
Algorithm 1 Iterative training of CAE, with removal of suspicious elements |
|
3.3.2. Plot-Level Classification of Time Series Anomalies
- “Candidate for relabeling”: we consider a plot as a candidate for relabeling when more than 75% of the pixel-wise time series within the field are of the same new class. We empirically chose the value of 75% as “edge cases“ can represent up to 20% of the time series-level mislabels within a field. Allowing a margin of error of approximately 5%, we thus reach the threshold of 75%.
- “Mis-split plot”: we consider a plot as “mis-split” if two different classes are present with the crops boundaries, according to each time series’ candidate new class, with each representing at least 40% of the plot size. The 40% criteria is also empirically found, as a field composed of at least two candidates classes, each representing 40% of its inner time series, will have 80% categorized as candidates classes, leaving up to 20% of the rest to potential edge cases.
- “Edge Cases”: we consider any other time series-level anomaly as edge cases. We believe they arise for multiple reasons, including differences in resolution between the labels and the satellite imagery, the preprocessing of Sentinel-1 data, which included boxcar despeckling, or approximate incorrect geolocation of labels/SAR data.
3.4. Correction of Mislabeled Crops
- The prior class of a given time series is not correct (i.e., class outlier detection). We model this using .
- A given time series belongs to the new candidate class (i.e., class belongingness detection). We model this using .
- Check that the parcel is among the least well reconstructed of its ground truth class, i.e., .
- Check that the parcel is among the best reconstructed of its new class,i.e., .
4. Numerical Validation of the Methodology
4.1. Quantitative Validation Scheme: A Controlled Disturbed Environment
4.2. Correction Performance, a Comparison with Supervised and Unsupervised Methods
- How many mislabels are correctly relabeled?This metric offers a measure of how many mislabels we expect to miss, given the chosen method. It provides an approximate of how many mistakes may be remaining in the cleaned crop type survey (without taking into account mistakes that may be added by the correcting algorithms themselves).
- Out of every relabels, how many are correct?Given a set of corrections, this metric provides an estimate of how many are erroneous. In other words, it is similar to estimating how many mistakes are introduced by the correcting algorithm.
5. Results, and Their Qualitative Validation
5.1. Results
- FARMSAR discovers 3 mis-split crops;
- our method classifies 81 crops as suspicious mislabels (around 5% out of the approx. 1600 crops of this half of the dataset). FARMSAR relabels 44 crops confidently, and 37 crops are to be inspected for potential erroneous labels.
5.2. Qualitative Validation: Sentinel-2 Imagery over the First Group of Crops
5.2.1. Validation of Relabels
5.2.2. Validation of Mis-Splits
Mis-Split Field n°1
Mis-Split Field n°2
Mis-Split Field n°3
- the two classes that are potentially seen in the mis-split crop are “cotton” and “pumpkin”.
- the separation is not as clear as the last two mis-split crops.
6. Discussion
- On a farmer’s side, FARMSAR provides a fast and reliable methodology to double-check the agricultural census of grown crops, leading to less risk-taking at the time of declaration.
- On the local administration side, FARMSAR provides a tool to monitor the quality of the delivered census. FARMSAR could facilitate the detection and extraction of anomalies in declarations.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
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Sentinel-1 Acquisitions Metadata | |
---|---|
Acquisition Mode | Interferometric Wide |
Polarisation | VV + VH |
Relative Orbit Number | 74 |
Wavelength | C-Band |
Orbit Pass | Ascending |
Near Incidence Angle | approx. 31.47° |
Far Incidence Angle | approx. 32.82° |
Acquisition Dates | 3 Jan. to 29 Dec. 2017 |
Location | 36°59′00.0″ N 6°06′00.0″ W |
Operation Layer | Number of Filters | Size of Each Filter | Stride Value | Padding Value | Ouput Vector Size | |
---|---|---|---|---|---|---|
Input time series | - | - | - | - | ||
Convolution Layer | 1D Convolution | 64 | 7 | 1 | 1 | |
ELU | - | - | - | - | ||
Pooling Layer | Max Pooling 1D | - | 2 | 2 | - | |
Convolution Layer | 1D Convolution | 128 | 5 | 1 | 0 | |
ELU | - | - | - | - | ||
Pooling Layer | Max Pooling 1D | - | 2 | 2 | - | |
Convolution Layer | 1D Convolution | 256 | 3 | 1 | 0 | |
ELU | - | - | - | - | ||
Pooling Layer | Max Pooling 1D | - | 2 | 2 | - | |
Flatten Layer | Flatten | - | - | - | - | 1280 |
FC Layer | Fully Connected | - | - | - | - | 128 |
ELU | - | - | - | - | 128 | |
FC Layer | Fully Connected | - | - | - | - | 64 |
ELU | - | - | - | - | 64 | |
FC Layer | Fully Connected | - | - | - | - | 32 |
ELU | - | - | - | - | 32 | |
Embedding Layer | Fully Connected | - | - | - | - | 1 |
ELU | - | - | - | - | 1 | |
FC Layer | Fully Connected | - | - | - | - | 32 |
ELU | - | - | - | - | 32 | |
FC Layer | Fully Connected | - | - | - | - | 64 |
ELU | - | - | - | - | 64 | |
FC Layer | Fully Connected | - | - | - | - | 128 |
ELU | - | - | - | - | 128 | |
Output Layer | Fully Connected | - | - | - | - | 122 |
Reshape | - | - | - | - |
Method | Parameterization |
---|---|
CAE | ADAM optimizer |
Learning Rate = 1 × 10 | |
Batch Size = 128 | |
Epochs = 20 |
Mislabels’ Proportion | Amount of Corrected Mislabels | Amount of Correct Relabels | ||||||
---|---|---|---|---|---|---|---|---|
F-CAE 1 | CAE | SVM | RF | F-CAE 1 | CAE | SVM | RF | |
1 | 0.62 | 0.92 | 0.90 | 0.95 | 0.95 | 0.71 | 0.52 | 0.56 |
5 | 0.62 | 0.90 | 0.85 | 0.94 | 0.97 | 0.90 | 0.80 | 0.86 |
10 | 0.59 | 0.88 | 0.77 | 0.90 | 0.98 | 0.92 | 0.79 | 0.91 |
15 | 0.53 | 0.81 | 0.67 | 0.87 | 0.94 | 0.84 | 0.78 | 0.90 |
20 | 0.44 | 0.71 | 0.59 | 0.83 | 0.92 | 0.77 | 0.70 | 0.89 |
25 | 0.42 | 0.67 | 0.48 | 0.73 | 0.89 | 0.73 | 0.55 | 0.85 |
30 | 0.31 | 0.59 | 0.38 | 0.60 | 0.86 | 0.71 | 0.49 | 0.77 |
Class | S2 Date (DD/MM/YYYY) | True ... | Mislabeled as ... | Relabeled as ... |
---|---|---|---|---|
Alfalfa | 12/04 | |||
11/07 | ||||
Carrot | 22/05 | |||
21/07 | ||||
Chickpea | 12/04 | ∅ | ||
01/06 | ∅ | |||
Cotton | 01/06 | |||
20/08 | ||||
Fallow | 12/04 | ∅ | ||
21/06 | ∅ | |||
Maize | 12/04 | ∅ | ||
21/06 | ∅ | |||
Onion | 12/04 | ∅ | ||
21/06 | ∅ | |||
Pepper | 12/04 | |||
20/08 | ||||
Potato | 12/04 | |||
20/08 | ||||
Quinoa | 12/04 | ∅ | ||
21/06 | ∅ | |||
Sugar Beet | 22/05 | |||
20/08 | ||||
Sunflower | 12/04 | ∅ | ||
01/06 | ∅ | |||
Sweet Potato | 21/06 | ∅ | ||
30/08 | ∅ | |||
Tomato | 12/04 | |||
21/06 | ||||
Wheat | 22/05 | ∅ | ||
01/06 | ∅ |
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Di Martino, T.; Guinvarc’h, R.; Thirion-Lefevre, L.; Colin, E. FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs. Remote Sens. 2023, 15, 35. https://doi.org/10.3390/rs15010035
Di Martino T, Guinvarc’h R, Thirion-Lefevre L, Colin E. FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs. Remote Sensing. 2023; 15(1):35. https://doi.org/10.3390/rs15010035
Chicago/Turabian StyleDi Martino, Thomas, Régis Guinvarc’h, Laetitia Thirion-Lefevre, and Elise Colin. 2023. "FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs" Remote Sensing 15, no. 1: 35. https://doi.org/10.3390/rs15010035
APA StyleDi Martino, T., Guinvarc’h, R., Thirion-Lefevre, L., & Colin, E. (2023). FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs. Remote Sensing, 15(1), 35. https://doi.org/10.3390/rs15010035