Comparing Machine and Deep Learning Methods for the Phenology-Based Classification of Land Cover Types in the Amazon Biome Using Sentinel-1 Time Series
<p>Location study area in the state of Amapá in Brazil and the South American continent (<b>A</b>). The image of the study area corresponds to the first component of the Minimum Fraction of Noise transformation considering the Sentinel 1 VH polarization time series in the period 2017–2020 (<b>B</b>).</p> "> Figure 2
<p>Methodological flowchart.</p> "> Figure 3
<p>Sentinel-1 time series denoising using the Savitzky and Golay (S-G) method in the Amazon savanna. The original data is the gray line, and the data smoothed with the S-G filter is the purple line. C-band backscattering differences in VH polarization correspond to seasonal biomass variations during the wet (high values) and dry (low values) seasons.</p> "> Figure 4
<p>Study area samples for training (cyan), validation (red), and test (orange).</p> "> Figure 5
<p>Mean temporal signatures of the 500 samples selected for water bodies (blue line), Ombrophilous Forest (gray line), and savannah (yellow line) considering VV (<b>A</b>) and VH (<b>B</b>) polarizations of Sentinel-1A. The curves display the standard deviation bars.</p> "> Figure 6
<p>Mean temporal trajectory of the 500 samples selected considering Sentinel-1 data with VV (<b>A</b>) and VH polarization (<b>B</b>) for the classes: land accretion areas with water to land conversion (orange line) and land erosion areas with land to water conversion (blue line). The curves display the standard deviation bars.</p> "> Figure 7
<p>Time series image sequences that show in the state of Amapá: (<b>A</b>) coastal erosion process, (<b>B</b>) coastal land accretion process, and (<b>C</b>) fluvial dynamic. The last image represents an RGB color composite (CC) composed of the images from January 2017, January 2019, and December 2020. The red colors in the images represent areas of land erosion and the blue ones of accretion.</p> "> Figure 8
<p>Average temporal signatures of the 500 samples selected for each seasonally flooded grasslands: sparse herbaceous (blue line) and grasslands with the presence of sparse woody (orange line) with VV (<b>A</b>) and VH (<b>B</b>) polarizations. In addition, grassland with a medium and dense herbaceous (black and sea green lines) with VV (<b>C</b>) and VH (<b>D</b>) polarizations. All graphs present the non-floodable shrub grassland time series (red line) compared with seasonally flooded areas. The curves display the standard deviation bars.</p> "> Figure 9
<p>Average temporal signatures (Sentinel 1 VV and VH radar signals) of 500 samples with their respective standard deviation bars for the following vegetation classes: (<b>A</b>,<b>E</b>) pioneer formations with the increase in the tree-shrub stratum (herbaceous (magenta line) > shrub (yellow line) > arboreal (green line)); (<b>B</b>,<b>F</b>) shrub grassland (dark purple line) and savanna/shrub savanna curve (light purple line); (<b>C</b>,<b>G</b>) two time series of ombrophilous forest (green lines) and mangroves (brown line) with the insertion of the savanna time series (red line) for comparison; and (<b>D</b>,<b>E</b>,<b>H</b>) agricultural planting (blue line) and eucalyptus plantation (orange line).</p> "> Figure 10
<p>McNemar’s statistical test at a significance level of 0.05 containing magenta cells represent paired methods significantly different from each other, and green cells describe similar results. The models are shown in numbered order: (1) Bidirectional Gated Recurrent Unit (Bi-GRU), (2) GRU, (3) Bidirectional Long Short-Term Memory (Bi-LSTM), (4) LSTM, (5) Random Forest, (6) XGBoost, (7) Support Vector Machine, (8) k-Nearest Neighbor, and (9) Multilayer Perceptron.</p> "> Figure 11
<p>Comparing the phenology-based classification of land-cover types with the highest and lowest accuracy metrics between the RNN (Bi-GRU and LSTM) and ML (SVM and k-NN) methods for the different datasets (VV only, VH only, and VV&VH) in a detail area.</p> "> Figure 12
<p>Land-cover map of the southeastern region of the State of Amapá using the Bi-directional Gated Recurrent Unit (Bi-GRU) method, which obtained the best accuracy measures. The dashed square corresponds to the area in <a href="#remotesensing-14-04858-f011" class="html-fig">Figure 11</a>.</p> ">
Abstract
:1. Introduction
- Describe phenological patterns of land cover/land use;
- Characterize erosion/accretion changes in coastal and fluvial environments;
- Evaluate the behavior of VV-only, VH-only, and both VV and VH (VV&VH) datasets in the differentiation of land-cover/land-use features;
- Compare the behavior of five traditional machine learning models (RF, XGBoost, SVM, k-NN, and MLP) and four RNN models (LSTM, Bi-LSTM, GRU, and Bidirectional GRU (Bi-GRU)) in time-series classification;
- Produce a land-cover/land-use map for the Amapá region.
2. Study Area
3. Materials and Methods
3.1. Data Preparation
3.2. Ground Truth and Sample Dataset
3.3. Image Classification
3.3.1. Traditional Machine Learning Methods
3.3.2. Recurrent Neural Network Architectures
3.4. Accuracy Assessment
4. Results
4.1. Temporal Backscattering Signatures
4.1.1. Water Bodies and Accretion/Erosion Changes due to Coastal and River Dynamics
4.1.2. Phenological Patterns
4.2. Comparison between RNN and Machine Learning Methods
4.3. Land-Cover/Land-Use Map
5. Discussion
5.1. Temporal Signatures of Water Bodies and Alterations by Land Accretion and Erosion Processes in Coastal and River Environments
5.2. Temporal Signatures of Vegetation
5.3. Classifier Comparison
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Parameter | Values | |
---|---|---|---|
ML | RF | bootstrap | True, False |
oob_score | True, False | ||
max_depth | 3, 5, 7 | ||
n_estimators | 50, 100, 200, 400 | ||
min_samples_split | 2, 3, 5 | ||
max_leaf_nodes | None, 2, 4 | ||
XGBoost | Learning_rate | 0.01, 0.05, 0.1 | |
Min_child_weight | 1, 3, 5, 7 | ||
gamma | 1, 3, 5, 7 | ||
Colsample_bytree | 0.4, 0.5, 0.6 | ||
Max_depth | 3, 5, 7 | ||
Reg_alpha | 0, 0.2, 0.3 | ||
Subsample | 0.6, 0.8 | ||
SVM | C | 0.5, 1, 2, 3, 5 | |
Degree | 2, 3, 4 | ||
Kernel | linear, rbf, poly | ||
MLP | Hidden_layer_sizes | (100,50), (200,100), (300,150) | |
activation | logistic, relu, tanh | ||
Learning_rate | 0.01, 0.001 | ||
Max_iter | 500, 1000 | ||
k-NN | N_neighbors | 5, 10, 15, 20 | |
Weights | uniform, distance | ||
DL | RNN models | Epochs | 5000 |
Dropout | 0.5 | ||
Optimizer | Adam | ||
Learning rate | 0.001 | ||
Loss function | Categorical cross-entropy | ||
Batch size | 1024 | ||
Hidden layers | 2 | ||
Hidden layer sizes | (366, 122) |
Model | VV | VH | VV&VH | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AO | P | R | F1 | AO | P | R | F1 | AO | P | R | F1 | ||
DL | Bi-GRU | 85.57 | 85.88 | 85.57 | 85.72 | 91.61 | 91.64 | 91.61 | 91.63 | 93.49 | 93.58 | 93.49 | 93.53 |
GRU | 85.1 | 85.21 | 85.1 | 85.15 | 90.51 | 90.73 | 90.51 | 90.62 | 93.18 | 93.34 | 93.18 | 93.3 | |
Bi-LSTM | 85.57 | 85.79 | 85.57 | 85.68 | 90.98 | 91.19 | 90.98 | 91.08 | 93.26 | 93.33 | 93.26 | 93.29 | |
LSTM | 85.02 | 85.23 | 85.02 | 85.12 | 90.59 | 90.63 | 90.59 | 90.61 | 93.1 | 93.19 | 93.1 | 93.15 | |
ML | RF | 81.33 | 82.26 | 81.33 | 81.79 | 87.53 | 87.79 | 87.23 | 87.66 | 90.67 | 90.94 | 90.67 | 90.8 |
XGBoost | 83.14 | 83.99 | 83.14 | 83.56 | 88.39 | 88.63 | 88.39 | 88.51 | 91.92 | 92.04 | 91.92 | 91.98 | |
SVM | 82.59 | 83.21 | 82.59 | 82.9 | 90.19 | 90.31 | 90.2 | 90.25 | 92.16 | 92.21 | 92.16 | 92.18 | |
k-NN | 78.75 | 80.43 | 78.75 | 79.58 | 85.33 | 86.22 | 85.33 | 85.77 | 88.94 | 88.25 | 88.94 | 89.37 | |
MLP | 83.77 | 84.1 | 83.77 | 83.93 | 88.78 | 89.81 | 88.78 | 89.29 | 90.82 | 91.2 | 90.82 | 91.01 |
VV | VH | VV + VH | |||||||
---|---|---|---|---|---|---|---|---|---|
Class | P | R | F-Score | P | R | F-Score | P | R | F-Score |
1–Water bodies | 100 * | 94.67 | 97.26 | 100 * | 96 | 97.96 | 100 * | 97.33 | 98.65 |
2–Land erosion | 91.14 | 96 | 93.51 | 92.41 | 97.33 | 94.81 | 92.50 | 98.67 | 95.48 |
3–Land accretion | 95.95 | 94.67 | 95.3 | 98.65 | 97.33 | 97.99 | 98.63 | 96.00 | 97.30 |
4–Sparse seasonally flooded grassland | 78.16 | 90.67 | 83.95 | 93.51 | 96.00 * | 94.74 | 96.00 | 96.00 * | 96.00 |
5–Dense seasonally flooded grassland 1 | 87.5 | 84.00 | 85.71 | 94.81 | 97.33 * | 96.05 | 97.33 | 97.33 * | 97.33 |
6–Dense seasonally flooded grassland 2 | 95.89 | 93.33 | 94.59 | 97.30 * | 96.00 * | 96.64* | 97.30 * | 96.00 * | 96.64 * |
7–Dense humid grassland and floodplain areas | 86.44 | 68.00 | 76.12 | 93.33 | 93.33 | 93.33 | 93.59 | 97.33 | 95.42 |
8–Pioneer herbaceous formation | 85.9 | 89.33 | 87.58 | 97.22 | 93.33 | 95.24 | 96.00 | 96.00 | 96.00 |
9–Pioneer shrub formation | 84.42 | 86.67 | 85.53 | 97.26 | 94.67 | 95.95 | 94.44 | 90.67 | 92.52 |
10–Pioneer arboreal formation | 71.25 | 76.00 | 73.55 | 85.9 | 89.33 | 87.58 | 91.55 | 86.67 | 89.04 |
11–Shrub grassland | 75.68 | 74.67 | 75.17 | 83.75 | 89.33 | 86.45 | 87.32 | 82.67 | 84.93 |
12–Savanna/shrub savanna | 88.31 | 90.67 | 89.47 | 93.67 | 98.67 | 96.1 | 92.59 | 100 | 96.15 |
13–Mangroves | 92.00 | 92.00 | 92.00 | 76.71 | 74.67 | 75.68 | 91.03 | 94.67 | 92.81 |
14–Forest 1 | 78.48 | 82.67 | 80.52 | 88.31 | 90.67 | 89.47 | 95.65 | 88.00 | 91.67 |
15–Forest 2 | 72.41 | 84.00 | 77.78 | 83.56 | 81.33 | 82.43 | 84.81 | 89.33 | 87.01 |
16–Agriculture plantations (soybean) | 94.44 | 90.67 | 92.52 | 95.83 | 92.00 | 93.88 | 97.22 | 93.33 | 95.24 |
17–Planted forest | 81.97 | 66.67 | 73.53 | 85.71 | 80.00 | 82.76 | 84.81 | 89.33 | 87.01 |
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Magalhães, I.A.L.; de Carvalho Júnior, O.A.; de Carvalho, O.L.F.; de Albuquerque, A.O.; Hermuche, P.M.; Merino, É.R.; Gomes, R.A.T.; Guimarães, R.F. Comparing Machine and Deep Learning Methods for the Phenology-Based Classification of Land Cover Types in the Amazon Biome Using Sentinel-1 Time Series. Remote Sens. 2022, 14, 4858. https://doi.org/10.3390/rs14194858
Magalhães IAL, de Carvalho Júnior OA, de Carvalho OLF, de Albuquerque AO, Hermuche PM, Merino ÉR, Gomes RAT, Guimarães RF. Comparing Machine and Deep Learning Methods for the Phenology-Based Classification of Land Cover Types in the Amazon Biome Using Sentinel-1 Time Series. Remote Sensing. 2022; 14(19):4858. https://doi.org/10.3390/rs14194858
Chicago/Turabian StyleMagalhães, Ivo Augusto Lopes, Osmar Abílio de Carvalho Júnior, Osmar Luiz Ferreira de Carvalho, Anesmar Olino de Albuquerque, Potira Meirelles Hermuche, Éder Renato Merino, Roberto Arnaldo Trancoso Gomes, and Renato Fontes Guimarães. 2022. "Comparing Machine and Deep Learning Methods for the Phenology-Based Classification of Land Cover Types in the Amazon Biome Using Sentinel-1 Time Series" Remote Sensing 14, no. 19: 4858. https://doi.org/10.3390/rs14194858
APA StyleMagalhães, I. A. L., de Carvalho Júnior, O. A., de Carvalho, O. L. F., de Albuquerque, A. O., Hermuche, P. M., Merino, É. R., Gomes, R. A. T., & Guimarães, R. F. (2022). Comparing Machine and Deep Learning Methods for the Phenology-Based Classification of Land Cover Types in the Amazon Biome Using Sentinel-1 Time Series. Remote Sensing, 14(19), 4858. https://doi.org/10.3390/rs14194858