# The Influence of Polarimetric Parameters and an Object-Based Approach on Land Cover Classification in Coastal Wetlands

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## Abstract

**:**

## 1. Introduction

## 2. Study Area and Datasets

**Figure 1.**Map of the study area (

**left**) and Pauli RGB image (

**right**). The region of interest is shown in the black dashed rectangle.

**Figure 2.**Photographs of eight typical land cover types taken in the study area. (

**a**) Grassland; (

**b**) river; (

**c**) sand; (

**d**) paddy rice; (

**e**) irrigable land; (

**f**) wetland vegetation; (

**g**) dry land; and (

**h**) fish pond.

## 3. Methodology

#### 3.1. Polarimetric Decomposition and Parameter Extraction

**S**can be vectorized to $\text{\kappa}$ with the Pauli basis.

Decomposition Method | Polarimetric Parameters | ||
---|---|---|---|

Pauli [22] | Pauli_a | Pauli_b | Pauli_c |

Krogager [24] | Krogager_KS | Krogager_KD | Krogager_KH |

Huynen [25] | Huynen_T11 | Huynen_T22 | Huynen_T33 |

Barnes1 [26] | Barnes1_T11 | Barnes1_T22 | Barnes1_T33 |

Barnes2 [26] | Barnes2_T11 | Barnes2_T22 | Barnes2_T33 |

Holm1 [27] | Holm1_T11 | Holm1_T22 | Holm1_T33 |

Holm2 [27] | Holm2_T11 | Holm2_T22 | Holm2_T33 |

VanZyl3 [28] | VanZyl3_Vol | VanZyl3_Odd | VanZyl3_Dbl |

Cloude [22] | Cloude_T11 | Cloude_T22 | Cloude_T33 |

H/A/Alpha [29] | H/A/A_T11 | H/A/A_T22 | H/A/A_T33 |

Freeman2 [30] | Freeman2_Vol | Freeman2_Ground | |

Freeman3 [31] | Freeman_Vol | Freeman_Odd | Freeman_Dbl |

Yamaguchi3 [32] | Yamaguchi3_Vol | Yamaguchi3_Odd | Yamaguchi3_Dbl |

Yamaguchi4 [33] | Yamaguchi4_Vol | Yamaguchi4_Odd | Yamaguchi4_Dbl |

Neumann [34] | Neumann_delta_mod | Neumann_delta_pha | Neumann_tau |

Touzi [35] | TSVM_alpha_s | TSVM_alpha_s1 | TSVM_alpha_s2 |

An_Yang3 [36] | An_Yang3_Vol | An_Yang3_Odd | An_Yang3_Dbl |

An_Yang4 [37] | An_Yang4_Vol | An_Yang4_Odd | An_Yang4_Dbl |

Arii3_NNED [38] | Arii3_NNED_Vol | Arii3_NNED_Odd | Arii3_NNED_Dbl |

Arii3_ANNED [39] | Arii3_ANNED_Vol | Arii3_ANNED_Odd | Arii3_ANNED_Odd |

#### 3.2. Object-Based Image Analysis and Feature Calculation

#### 3.3. Decision Tree Algorithm

#### 3.4. Methods for Comparison

## 4. Results and Discussion

#### 4.1. Constructed Decision Tree and Selected Polarimetric Parameters

**Figure 5.**Structure of the decision tree. SE, Shannon entropy; KG, Krogager_Kd; B2, Barnes2_T33; HAA, HAAlpha_T11; PF, Polarization Fraction; VZ, VanZyl3_Vol; B1, Barnes1_T33; NDM, Neuman_delta_mod; SD, the standard deviation of entropy; DS, the distance to the neighbor objects.

#### 4.2. LULC Classification Results

**Figure 7.**Classification results from: (

**a**) the proposed method; (

**b**) the Wishart supervised classification; (

**c**) the proposed method without polarimetric parameters; (

**d**) the proposed method without an object-based segmentation; (

**e**) the proposed method without textural and geometric information; and (

**f**) the proposed method using the nearest neighbor classifier instead of the decision tree algorithm.

**Table 2.**Classification accuracy. SA, sand; DL, dry land; FP, fish pond; GL, grassland; IL, irrigable land; PR, paddy rice; RI, river; RO, road; S, sea; W, wetland vegetation; WSC, Wishart supervised classification; PWPP, proposed method without polarimetric parameters; PWOS, proposed method without object-based segmentation; PWTG, proposed method without textural and geometric information; PNNC, proposed method with the nearest-neighbor classifier.

Method | Accuracy | Class | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|

SA | DL | FP | GL | IL | PR | RI | RO | S | W | ||

Proposed method | PA (%) | 83.2 | 88.8 | 86.0 | 84.6 | 80.3 | 84.9 | 95.3 | 92.0 | 88.8 | 93.5 |

UA (%) | 89.2 | 87.1 | 89.5 | 86.4 | 87.4 | 90.9 | 85.5 | 85.2 | 90.0 | 80.7 | |

OA (%) | 87.3 | ||||||||||

WSC | PA (%) | 84.3 | 77.9 | 42.8 | 75.4 | 76.1 | 74.1 | 30.3 | 89.3 | 52.3 | 94.6 |

UA (%) | 83.4 | 87.2 | 6.6 | 78.8 | 87.4 | 71.6 | 80.7 | 87.9 | 17.3 | 72.5 | |

OA (%) | 66.6 | ||||||||||

PWPP | PA (%) | 88.6 | 79.4 | 73.6 | 74.4 | 67.9 | 75.7 | 73.4 | 91.1 | 49.2 | 89.3 |

UA (%) | 83.0 | 84.3 | 24.6 | 67.6 | 90.6 | 71.6 | 80.7 | 92.1 | 79.6 | 72.5 | |

OA (%) | 74.0 | ||||||||||

PWOS | PA (%) | 90.6 | 78.8 | 92.6 | 72.8 | 69.4 | 82.4 | 51.6 | 90.7 | 55.6 | 92.1 |

UA (%) | 79.6 | 84.8 | 82.5 | 67.6 | 90.6 | 78.8 | 23.4 | 93.2 | 89.0 | 77.6 | |

OA (%) | 77.1 | ||||||||||

PWTG | PA (%) | 87.8 | 79.9 | 61.6 | 71.8 | 71.2 | 75.7 | 75.6 | 90.1 | 47.6 | 84.5 |

UA (%) | 84.4 | 87.1 | 14.0 | 65.6 | 87.4 | 71.7 | 80.8 | 92.1 | 84.1 | 72.5 | |

OA (%) | 73.2 | ||||||||||

PNNC | PA (%) | 87.6 | 80.5 | 94.6 | 70.7 | 65.5 | 89.5 | 65.7 | 90.3 | 82.6 | 94.4 |

UA (%) | 79.5 | 83.8 | 69.7 | 67.1 | 91.7 | 81.2 | 81.8 | 88.4 | 84.6 | 77.8 | |

OA (%) | 80.5 |

## 5. Conclusions

## Supplementary Files

Supplementary File 1## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Chen, Y.; He, X.; Wang, J.; Xiao, R. The Influence of Polarimetric Parameters and an Object-Based Approach on Land Cover Classification in Coastal Wetlands. *Remote Sens.* **2014**, *6*, 12575-12592.
https://doi.org/10.3390/rs61212575

**AMA Style**

Chen Y, He X, Wang J, Xiao R. The Influence of Polarimetric Parameters and an Object-Based Approach on Land Cover Classification in Coastal Wetlands. *Remote Sensing*. 2014; 6(12):12575-12592.
https://doi.org/10.3390/rs61212575

**Chicago/Turabian Style**

Chen, Yuanyuan, Xiufeng He, Jing Wang, and Ruya Xiao. 2014. "The Influence of Polarimetric Parameters and an Object-Based Approach on Land Cover Classification in Coastal Wetlands" *Remote Sensing* 6, no. 12: 12575-12592.
https://doi.org/10.3390/rs61212575