# Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Data

## 3. Methodology

#### 3.1. Selection of the 21 Tiles

#### 3.2. Image Segmentation

#### 3.3. Segmentation Optimization

#### 3.3.1. Existing Unsupervised Segmentation Evaluation Metrics

#### 3.3.2. Metric Proposal Based on Absolute Difference (AD)

#### 3.3.3. Unsupervised Segmentation Optimization

- The domain space (minimum and maximum values) of each input parameter. The domain space of scale was defined as 20 and 200, for shape 0.0 and 0.9, and for compactness 0.0 and 1.0. These parameter ranges were also used by Tetteh et al. [47] in their approach.
- An objective function to optimize. For our study, the objective function to optimize is f(x), where x is a parameter combination of scale, shape, and compactness. The function takes the parameter combination, performs image segmentation, computes the GS of the segmentation output, and finally returns the GS.
- A surrogate model for the objective function. To build the surrogate model, one has to first define a prior probability distribution that captures the prior behavior of the objective function. We chose Gaussian Processes (GP) [50] as the prior probability distribution. Then, some initial parameter combinations together with their corresponding GS are used to initialize the whole optimization process. We used 125 parameter combinations as initialization samples. These 125 parameter combinations were selected in a way to ensure uniform and representative distribution over each parameter space. For scale, the values were (40, 80, 120, 160, 200), and for both shape and compactness, the values were (0.1, 0.3, 0.5, 0.7, 0.9). The grid search method was used to calculate the corresponding GS for the 125 samples. These samples were used to update the GP to obtain posterior probability distribution over the objective function.
- An acquisition function to be used in sampling new parameter combinations to be evaluated with the objective function. For the acquisition function, we used expected improvement (EI) [51]. EI is used to iteratively select new parameter combinations with the highest probability of optimizing the objection function. We sampled 50 new parameter combinations with the EI function in 50 iterations. At each iteration, out of 10,000 parameter combinations randomly sampled from the domain space, the combination with the highest likelihood of improving upon the current optimal parameter combination is identified by the EI function using the current posterior probability distribution. Then, this identified parameter combination is evaluated with the objective function, and the corresponding GS is used to update the current posterior probability distribution. In all, 175 combinations were used within the Bayesian optimization approach to identify the optimal one.

#### 3.4. Empirical Discrepancy Measures

## 4. Results

#### 4.1. Optimal Segmentation Based on Default Optimization

#### 4.2. Optimal Segmentation Based on Bayesian Optimization

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

Tile | Image Date | Agric. Land Cover | No. of Land-Use Types | No. of LPIS Parcels | Min. Area (Ha) | Max. Area (Ha) | Mean Area (Ha) |
---|---|---|---|---|---|---|---|

T1 | 20 May 2018 | 62.29% | 12 | 1308 | 0.232 | 25.777 | 4.097 |

T2 | 5 May 2018 | 62.76% | 8 | 1344 | 0.173 | 21.726 | 2.398 |

T3 | 8 May 2018 | 80.91% | 4 | 2341 | 0.191 | 53.279 | 2.924 |

T4 | 7 May 2018 | 53.30% | 14 | 1671 | 0.169 | 35.281 | 2.522 |

T5 | 5 May 2018 | 76.83% | 14 | 1957 | 0.180 | 18.888 | 3.219 |

T6 | 5 May 2018 | 79.61% | 11 | 2500 | 0.168 | 22.639 | 2.565 |

T7 | 5 May 2018 | 68.08% | 16 | 2140 | 0.203 | 25.181 | 2.579 |

T8 | 8 May 2018 | 50.17% | 12 | 1100 | 0.199 | 30.562 | 3.704 |

T9 | 5 May 2018 | 70.43% | 11 | 1613 | 0.190 | 44.890 | 3.699 |

T10 | 5 May 2018 | 70.13% | 12 | 2441 | 0.177 | 26.253 | 2.243 |

T11 | 5 May 2018 | 71.41% | 15 | 1625 | 0.172 | 30.012 | 3.709 |

T12 | 5 May 2018 | 90.15% | 12 | 1798 | 0.171 | 50.494 | 3.127 |

T13 | 5 May 2018 | 92.31% | 12 | 1221 | 0.181 | 64.772 | 6.203 |

T14 | 5 May 2018 | 63.62% | 15 | 1894 | 0.176 | 26.646 | 2.637 |

T15 | 5 May 2018 | 36.63% | 15 | 809 | 0.203 | 23.687 | 3.580 |

T16 | 5 May 2018 | 58.45% | 14 | 1752 | 0.181 | 29.022 | 2.781 |

T17 | 5 May 2018 | 61.10% | 14 | 1538 | 0.180 | 28.160 | 2.994 |

T18 | 5 May 2018 | 37.26% | 13 | 729 | 0.193 | 28.514 | 4.158 |

T19 | 7 May 2018 | 14.29% | 8 | 420 | 0.217 | 25.855 | 2.471 |

T20 | 7 May 2018 | 33.35% | 13 | 744 | 0.191 | 36.408 | 3.111 |

T21 | 7 May 2018 | 90.84% | 11 | 1340 | 0.213 | 62.730 | 5.883 |

**Table A2.**Empirical discrepancy measures computed for each optimal segmentation result identified by the AD and Böck metrics based on the default optimization (shape = 0.1, compactness = 0.5). The bold-faced texts within the body of the table are the optimal results.

Tile | Scale | Shape | Compactness | QR | OR | UR | RMS | Metric |
---|---|---|---|---|---|---|---|---|

T1 | 190 | 0.100 | 0.500 | 55.53% | 0.115 | 0.375 | 0.278 | AD |

T1 | 300 | 0.100 | 0.500 | 38.42% | 0.057 | 0.597 | 0.424 | Böck |

T2 | 80 | 0.100 | 0.500 | 36.94% | 0.334 | 0.467 | 0.406 | AD |

T2 | 70 | 0.100 | 0.500 | 36.07% | 0.387 | 0.427 | 0.407 | Böck |

T3 | 150 | 0.100 | 0.500 | 57.91% | 0.183 | 0.304 | 0.251 | Böck |

T3 | 140 | 0.100 | 0.500 | 57.80% | 0.192 | 0.296 | 0.250 | AD |

T4 | 200 | 0.100 | 0.500 | 28.33% | 0.121 | 0.685 | 0.492 | AD |

T4 | 280 | 0.100 | 0.500 | 20.84% | 0.076 | 0.779 | 0.553 | Böck |

T5 | 160 | 0.100 | 0.500 | 44.69% | 0.163 | 0.477 | 0.356 | AD |

T5 | 200 | 0.100 | 0.500 | 39.00% | 0.122 | 0.563 | 0.407 | Böck |

T6 | 170 | 0.100 | 0.500 | 42.45% | 0.169 | 0.502 | 0.375 | AD |

T6 | 180 | 0.100 | 0.500 | 41.24% | 0.161 | 0.520 | 0.385 | Böck |

T7 | 190 | 0.100 | 0.500 | 32.84% | 0.128 | 0.631 | 0.455 | AD |

T7 | 270 | 0.100 | 0.500 | 25.46% | 0.084 | 0.729 | 0.519 | Böck |

T8 | 120 | 0.100 | 0.500 | 48.77% | 0.274 | 0.339 | 0.308 | AD |

T8 | 170 | 0.100 | 0.500 | 41.78% | 0.150 | 0.513 | 0.378 | Böck |

T9 | 170 | 0.100 | 0.500 | 44.88% | 0.215 | 0.446 | 0.350 | AD |

T9 | 300 | 0.100 | 0.500 | 33.86% | 0.101 | 0.635 | 0.454 | Böck |

T10 | 180 | 0.100 | 0.500 | 36.66% | 0.143 | 0.584 | 0.425 | AD |

T10 | 210 | 0.100 | 0.500 | 33.06% | 0.126 | 0.631 | 0.455 | Böck |

T11 | 230 | 0.100 | 0.500 | 35.67% | 0.127 | 0.595 | 0.430 | AD |

T11 | 230 | 0.100 | 0.500 | 35.67% | 0.127 | 0.595 | 0.430 | Böck |

T12 | 150 | 0.100 | 0.500 | 40.77% | 0.209 | 0.504 | 0.386 | AD |

T12 | 270 | 0.100 | 0.500 | 27.78% | 0.126 | 0.697 | 0.501 | Böck |

T13 | 240 | 0.100 | 0.500 | 42.42% | 0.177 | 0.495 | 0.372 | AD |

T13 | 300 | 0.100 | 0.500 | 35.18% | 0.142 | 0.601 | 0.436 | Böck |

T14 | 160 | 0.100 | 0.500 | 36.03% | 0.162 | 0.574 | 0.422 | AD |

T14 | 280 | 0.100 | 0.500 | 21.74% | 0.077 | 0.768 | 0.546 | Böck |

T15 | 220 | 0.100 | 0.500 | 32.40% | 0.090 | 0.648 | 0.463 | AD |

T15 | 300 | 0.100 | 0.500 | 22.41% | 0.062 | 0.764 | 0.542 | Böck |

T16 | 180 | 0.100 | 0.500 | 38.46% | 0.114 | 0.566 | 0.408 | AD |

T16 | 280 | 0.100 | 0.500 | 26.20% | 0.064 | 0.723 | 0.513 | Böck |

T17 | 200 | 0.100 | 0.500 | 31.89% | 0.137 | 0.634 | 0.459 | AD |

T17 | 240 | 0.100 | 0.500 | 27.14% | 0.111 | 0.700 | 0.501 | Böck |

T18 | 200 | 0.100 | 0.500 | 47.11% | 0.167 | 0.434 | 0.329 | Böck |

T18 | 190 | 0.100 | 0.500 | 47.06% | 0.168 | 0.427 | 0.325 | AD |

T19 | 210 | 0.100 | 0.500 | 37.29% | 0.092 | 0.595 | 0.426 | AD |

T19 | 50 | 0.100 | 0.500 | 36.58% | 0.552 | 0.207 | 0.417 | Böck |

T20 | 220 | 0.100 | 0.500 | 29.21% | 0.123 | 0.676 | 0.486 | AD |

T20 | 260 | 0.100 | 0.500 | 27.79% | 0.102 | 0.698 | 0.499 | Böck |

T21 | 270 | 0.100 | 0.500 | 43.91% | 0.133 | 0.499 | 0.365 | AD |

T21 | 300 | 0.100 | 0.500 | 40.52% | 0.117 | 0.546 | 0.395 | Böck |

**Table A3.**Empirical discrepancy measures computed for the unsupervised Bayesian optimization approaches based on the Böck and AD metrics, and the supervised Bayesian optimization approach (SUP) that was used to maximize the QR measure. The bold-faced texts within the body of the table are the optimal results.

Tile | Scale | Shape | Compactness | QR | OR | UR | RMS | Metric |
---|---|---|---|---|---|---|---|---|

T1 | 51 | 0.900 | 0.966 | 69.17% | 0.117 | 0.224 | 0.178 | SUP |

T1 | 160 | 0.300 | 0.500 | 57.47% | 0.126 | 0.349 | 0.263 | AD |

T1 | 200 | 0.841 | 0.917 | 34.39% | 0.035 | 0.648 | 0.459 | Böck |

T2 | 40 | 0.900 | 0.300 | 42.04% | 0.219 | 0.479 | 0.372 | SUP |

T2 | 42 | 0.792 | 0.176 | 40.28% | 0.309 | 0.429 | 0.374 | AD |

T2 | 56 | 0.415 | 0.192 | 37.40% | 0.402 | 0.395 | 0.398 | Böck |

T3 | 77 | 0.842 | 0.906 | 68.46% | 0.117 | 0.235 | 0.186 | SUP |

T3 | 117 | 0.420 | 1.000 | 62.79% | 0.164 | 0.263 | 0.219 | AD |

T3 | 138 | 0.279 | 0.175 | 59.14% | 0.165 | 0.304 | 0.245 | Böck |

T4 | 34 | 0.900 | 0.410 | 50.84% | 0.290 | 0.297 | 0.293 | SUP |

T4 | 116 | 0.655 | 1.000 | 38.04% | 0.121 | 0.576 | 0.416 | AD |

T4 | 174 | 0.666 | 0.753 | 24.88% | 0.076 | 0.738 | 0.524 | Böck |

T5 | 42 | 0.900 | 0.783 | 58.78% | 0.205 | 0.273 | 0.242 | SUP |

T5 | 132 | 0.468 | 0.701 | 47.21% | 0.149 | 0.459 | 0.341 | AD |

T5 | 162 | 0.395 | 0.452 | 42.52% | 0.124 | 0.524 | 0.381 | Böck |

T6 | 40 | 0.900 | 0.500 | 57.67% | 0.225 | 0.269 | 0.248 | SUP |

T6 | 127 | 0.422 | 0.083 | 46.98% | 0.172 | 0.442 | 0.335 | AD |

T6 | 144 | 0.377 | 0.000 | 46.05% | 0.161 | 0.466 | 0.348 | Böck |

T7 | 40 | 0.900 | 0.500 | 55.70% | 0.209 | 0.307 | 0.263 | SUP |

T7 | 183 | 0.088 | 0.401 | 35.14% | 0.142 | 0.601 | 0.436 | AD |

T7 | 178 | 0.686 | 0.611 | 29.39% | 0.071 | 0.692 | 0.492 | Böck |

T8 | 46 | 0.853 | 0.665 | 56.91% | 0.261 | 0.240 | 0.251 | SUP |

T8 | 120 | 0.100 | 0.300 | 49.20% | 0.261 | 0.339 | 0.303 | AD |

T8 | 160 | 0.300 | 0.100 | 43.71% | 0.145 | 0.499 | 0.367 | Böck |

T9 | 56 | 0.900 | 0.548 | 56.93% | 0.191 | 0.310 | 0.258 | SUP |

T9 | 129 | 0.398 | 1.000 | 49.61% | 0.212 | 0.384 | 0.310 | AD |

T9 | 200 | 0.300 | 0.500 | 41.28% | 0.148 | 0.532 | 0.390 | Böck |

T10 | 40 | 0.900 | 0.700 | 54.15% | 0.196 | 0.336 | 0.275 | SUP |

T10 | 189 | 0.000 | 0.380 | 37.43% | 0.152 | 0.573 | 0.419 | AD |

T10 | 184 | 0.587 | 0.633 | 33.58% | 0.084 | 0.641 | 0.457 | Böck |

T11 | 50 | 0.900 | 0.699 | 58.31% | 0.200 | 0.277 | 0.241 | SUP |

T11 | 200 | 0.100 | 0.900 | 40.52% | 0.143 | 0.528 | 0.386 | AD |

T11 | 108 | 0.900 | 0.777 | 38.50% | 0.073 | 0.595 | 0.424 | Böck |

T12 | 40 | 0.900 | 0.100 | 49.05% | 0.254 | 0.354 | 0.308 | SUP |

T12 | 163 | 0.000 | 0.605 | 38.73% | 0.197 | 0.536 | 0.404 | AD |

T12 | 200 | 0.500 | 0.700 | 33.57% | 0.119 | 0.635 | 0.457 | Böck |

T13 | 63 | 0.900 | 0.371 | 54.74% | 0.231 | 0.293 | 0.264 | SUP |

T13 | 151 | 0.643 | 0.272 | 47.92% | 0.168 | 0.434 | 0.329 | AD |

T13 | 165 | 0.819 | 0.614 | 41.67% | 0.091 | 0.551 | 0.395 | Böck |

T14 | 42 | 0.900 | 0.576 | 53.68% | 0.204 | 0.328 | 0.273 | SUP |

T14 | 120 | 0.500 | 0.100 | 38.92% | 0.156 | 0.539 | 0.397 | AD |

T14 | 200 | 0.700 | 0.100 | 21.35% | 0.059 | 0.778 | 0.552 | Böck |

T15 | 40 | 0.900 | 0.300 | 61.17% | 0.200 | 0.252 | 0.228 | SUP |

T15 | 63 | 0.900 | 0.428 | 52.67% | 0.106 | 0.421 | 0.307 | AD |

T15 | 109 | 0.900 | 0.000 | 29.95% | 0.064 | 0.687 | 0.488 | Böck |

T16 | 45 | 0.842 | 0.923 | 59.96% | 0.206 | 0.251 | 0.229 | SUP |

T16 | 101 | 0.652 | 0.762 | 47.17% | 0.116 | 0.470 | 0.342 | AD |

T16 | 154 | 0.569 | 0.621 | 35.65% | 0.086 | 0.615 | 0.439 | Böck |

T17 | 45 | 0.900 | 0.632 | 54.49% | 0.205 | 0.320 | 0.269 | SUP |

T17 | 200 | 0.104 | 0.192 | 31.68% | 0.133 | 0.637 | 0.460 | AD |

T17 | 185 | 0.603 | 0.800 | 28.57% | 0.093 | 0.691 | 0.493 | Böck |

T18 | 57 | 0.889 | 0.897 | 59.15% | 0.199 | 0.265 | 0.234 | SUP |

T18 | 116 | 0.653 | 0.370 | 49.68% | 0.172 | 0.398 | 0.307 | AD |

T18 | 160 | 0.700 | 0.300 | 39.16% | 0.094 | 0.572 | 0.410 | Böck |

T19 | 54 | 0.730 | 1.000 | 53.04% | 0.262 | 0.290 | 0.276 | SUP |

T19 | 40 | 0.900 | 0.700 | 51.35% | 0.221 | 0.343 | 0.288 | AD |

T19 | 40 | 0.601 | 0.000 | 42.72% | 0.460 | 0.223 | 0.362 | Böck |

T20 | 40 | 0.900 | 0.900 | 53.31% | 0.221 | 0.319 | 0.274 | SUP |

T20 | 200 | 0.154 | 0.961 | 34.98% | 0.131 | 0.604 | 0.437 | AD |

T20 | 200 | 0.700 | 0.500 | 24.48% | 0.067 | 0.743 | 0.528 | Böck |

T21 | 63 | 0.899 | 0.868 | 64.99% | 0.157 | 0.231 | 0.198 | SUP |

T21 | 170 | 0.627 | 0.582 | 48.55% | 0.129 | 0.447 | 0.329 | AD |

T21 | 200 | 0.813 | 0.173 | 39.54% | 0.074 | 0.579 | 0.412 | Böck |

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**Figure 1.**The study sites (tiles) overlaid on a mosaic of cloud-free and non-masked Sentinel-2 images captured in May 2018. The coordinates are in UTM Zone 32N (EPSG:32632).

**Figure 2.**The simplified workflow we used in this study. Böck refers to the unsupervised segmentation evaluation metric proposed by Böck et al. [43], and absolute difference (AD) is the modified version we proposed in this study.

**Figure 3.**Simulated reference and segmentation data. The reference dataset is represented by (

**a**). Three different corresponding segmentation results are represented by (

**b**–

**d**), respectively. Each row in each dataset represents a segment; hence, there are four segments in all.

**Figure 4.**The quality rate (QR) measure computed for each optimal segmentation result identified by the AD and Böck metrics based on the default optimization (shape = 0.1, compactness = 0.5).

**Figure 5.**Examples of segments identified as optimal at T1 using the default shape and compactness parameters. (

**a**) An example based on the optimal segmentation identified by the Böck metric showing massive under-segmentation and (

**b**) based on the AD metric, which shows a better delineation of the agricultural parcels with lower under-segmentation compared to Böck. The coordinates are in UTM Zone 32N (EPSG:32632).

**Figure 6.**The normalized average area-weighted variance (nWV), Moran’s I (MI), normalized Moran’s I (nMI), and global score (GS) computed for each scale at T1 based on (

**a**) the Böck metric and (

**b**) the AD metric.

**Figure 7.**The normalized average area-weighted variance (nWV), Moran’s I (MI), normalized Moran’s I (nMI), and global score (GS) computed for each scale at T3 based on (

**a**) the Böck metric and (

**b**) the AD metric.

**Figure 8.**The quality rate (QR) measure computed for the unsupervised Bayesian optimization approaches based on the Böck and AD metrics, and the supervised Bayesian optimization (SUP) approach that was used to maximize the QR measure by Tetteh et al. [47].

**Figure 9.**The outcome of the three Bayesian optimization approaches at T1. The Sentinel-2 image is shown by (

**a**). The optimal segments as identified by (

**b**) the Böck metric, (

**c**) the supervised Bayesian optimization approach, and (

**d**) the AD metric are symbolized by their respective QR measures. The coordinates are in UTM Zone 32N (EPSG:32632).

**Figure 10.**The outcome of the three Bayesian optimization approaches at T2. The Sentinel-2 image is shown by (

**a**). The optimal segments as identified by (

**b**) the Böck metric, (

**c**) the supervised Bayesian optimization approach, and (

**d**) the AD metric are symbolized by their respective QR measures. The coordinates are in UTM Zone 32N (EPSG:32632).

**Figure 11.**The outcome of the three Bayesian optimization approaches at T17. The Sentinel-2 image is shown by (

**a**). The optimal segments as identified by (

**b**) the Böck metric, (

**c**) the supervised Bayesian optimization approach, and (

**d**) the AD metric are symbolized by their respective QR measures. The coordinates are in UTM Zone 32N (EPSG:32632).

**Figure 12.**An example of segments created at T1 using the unsupervised Bayesian optimization approach based on (

**a**) the Böck metric and (

**b**) the AD metric. (

**c**) Segments generated by the supervised Bayesian optimization approach (SUP) based on the QR metric. The coordinates are in UTM Zone 32N (EPSG:32632).

**Figure 13.**Correlation between the number of land use (LU) types and the difference in QR between the supervised benchmark results and the unsupervised Bayesian optimization approaches based on (

**a**) the Böck metric and (

**b**) the AD metric.

**Table 1.**The Moran’s I (MI), normalized MI (nMI), normalized weighted variance (nWV), and global score (GS) of the Böck metric computed for the simulated data at Figure 3. The bold-faced text within the body of the table is the optimal result.

Identifier | MI | nMI | nWV | GS (Böck) |
---|---|---|---|---|

Figure 3b | 0.400 | 0.700 | 0.375 | 1.075 |

Figure 3c | −0.018 | 0.491 | 0.698 | 1.189 |

Figure 3d | −0.667 | 0.167 | 0.875 | 1.042 |

**Table 2.**The MI, nWV, and GS of the AD metric computed for the simulated data in Figure 3. The bold-faced text within the body of the table is the optimal result.

Measure | Formula | Range | Source |
---|---|---|---|

Quality rate (QR) | $\frac{{{\displaystyle \sum}}_{i=1}^{n}Area\left({Y}_{i}\right)\ast \frac{Area\left({X}_{i}{{\displaystyle \cap}}^{}{Y}_{i}\right)}{Area\left({X}_{i}{{\displaystyle \cup}}^{}{Y}_{i}\right)}}{{{\displaystyle \sum}}_{i=1}^{n}Area\left({Y}_{i}\right)}$ | 0 (worst) to 1 (perfect) segmentation | [56] |

Over-segmentation (OR) | $1-\frac{{{\displaystyle \sum}}_{i=1}^{n}Area\left({Y}_{i}\right)\ast \frac{Area\left({X}_{i}{{\displaystyle \cap}}^{}{Y}_{i}\right)}{Area\left({X}_{i}\right)}}{{{\displaystyle \sum}}_{i=1}^{n}Area\left({Y}_{i}\right)}$ | 0 (perfect) to 1 (worst) segmentation | [57] |

Under-segmentation (UR) | $1-\frac{{{\displaystyle \sum}}_{i=1}^{n}Area\left({Y}_{i}\right)\ast \frac{Area\left({X}_{i}{{\displaystyle \cap}}^{}{Y}_{i}\right)}{Area\left({Y}_{i}\right)}}{{{\displaystyle \sum}}_{i=1}^{n}Area\left({Y}_{i}\right)}$ | 0 (perfect) to 1 (worst) segmentation | [57] |

Root mean square (RMS) | $\sqrt{\frac{O{R}^{2}+U{R}^{2}}{2}}$ | 0 (perfect) to 1 (worst) segmentation | [56] |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Tetteh, G.O.; Gocht, A.; Schwieder, M.; Erasmi, S.; Conrad, C. Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes. *Remote Sens.* **2020**, *12*, 3096.
https://doi.org/10.3390/rs12183096

**AMA Style**

Tetteh GO, Gocht A, Schwieder M, Erasmi S, Conrad C. Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes. *Remote Sensing*. 2020; 12(18):3096.
https://doi.org/10.3390/rs12183096

**Chicago/Turabian Style**

Tetteh, Gideon Okpoti, Alexander Gocht, Marcel Schwieder, Stefan Erasmi, and Christopher Conrad. 2020. "Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes" *Remote Sensing* 12, no. 18: 3096.
https://doi.org/10.3390/rs12183096