# Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015

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## Abstract

**:**

## 1. Introduction

## 2. Study Area

## 3. Material and Methods

#### 3.1. Data Sources

#### 3.2. Classification Methods

#### 3.2.1. Maximum Likelihood

#### 3.2.2. Random Forest

#### 3.2.3. Support Vector Machines

#### 3.2.4. Sequential Maximum A Posteriori

#### 3.3. Multi-Seasonal Approach

#### 3.4. Feature Sets

#### 3.5. Training and Validation Areas

- Mapa de Cultivos y Aprovechamientos (crops and land-use map) published by the Spanish Ministerio de Agricultura, Pesca y Alimentación (Ministry of Agriculture, Fisheries and Food) with field data collected between 2001 and 2007 at a 1:50,000 scale.
- Corine Land Cover maps [24] for 2000 and 2006 at a 1:200,000 scale.
- 2002 orthophotography from the Sistema de Información Geográfica de Parcelas Agrícolas (Agricultural Plots Geographic Information System) project at a 1:5000 scale by the Spanish Ministerio de Agricultura, Pesca y Alimentación (Ministry of Agriculture, Fisheries and Food).
- Orthophotography series available in the Instituto Cartográfico de Valencia (Cartographic Institute of Valencia) and the Plan Nacional de Ortofotografía Aérea (Spanish Orthophotography National Plan, PNOA) for 2005, 2007 and 2012 at a 1:10,000 scale by the Spanish Instituto Geográfico Nacional (National Geographic Institute).
- Orthophotography from the PNOA for 2009 and 2014 at a 1:5000 scale.

#### 3.6. Classification Process

#### 3.7. Validation of Classifications and Evaluation of Hypothesis

- To evaluate how the results improve when RF and SVM parameters are optimized, a factorial ANOVA was conducted to compare the effects of the classification method (method), optimization (optimized), feature sets (varset) and the interactions between them. method included two levels (RF; SVM); optimized included two levels (Yes; No); and varset three levels (Sp: Spectral information; SpTex: Spectral and Textural information; SpTexRel: Spectral, Textural and contextual information). In this case, classifications were performed using the maximum number of images available per year: four in 2000, 2001, 2009, 2010, 2014, 2015; three in 2002, 2003, 2011, 2013; and two in 2004, 2005, 2006, 2007 and 2008. That makes 180 classifications.
- To evaluate how classification accuracy improves in the final models, a factorial ANOVA was conducted to compare the main effects of method, varset, the number of seasonal images (season) and the interaction effect between them. In this case, method included four levels (RF; SVM; ML; SMAP) and season four levels (One season; Two seasons; Three seasons; Four seasons). In this case, only the years when 4 images were available (2000, 2001, 2009, 2010, 2014 and 2015) were taken into account, making 288 classifications.

## 4. Results and Discussion

#### 4.1. Classification of 2009 Image

#### 4.2. Parameter Optimization

#### 4.3. Global Validation

#### 4.4. Per Class Validation

#### 4.5. Visual Validation

## 5. Conclusions

## Supplementary Materials

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Study area with the 2006 Corine Land Cover map overlayed [24].

**Figure 2.**Methodological flowchart. SMAP, Sequential Maximum A Posteriori; ICV, Cartographic Institute of Valencia; PNOA, Spanish Orthophotography National Plan.

**Figure 3.**Kappa values and 95% confidence intervals obtained in the 2009 classification. Number of classifications: 84.

**Figure 4.**Kappa series with 95% confidence intervals for the RF and SVM algorithms with and without optimization. Number of classifications: 180.

**Figure 5.**Main effects and simple effects of significant factors (mean and 95% confidence interval). Significantly different groups (Tukey–Kramer contrast using heteroscedasticity-consistent covariance matrix of the parameters (HC3), alpha = 0.05) are represented by different letters. Number of classifications: 180.

**Figure 6.**Distributions of kappa values obtained with the 12 combinations of algorithms and datasets in the years when four-season imagery was available (2000, 2001, 2009, 2010, 2014 and 2015). Sp: Spectral features; SpTex: Spectral and Textural features; SpTexTer: Spectral, Textural and Terrain features.

**Figure 7.**Validation kappa series with 95% confidence intervals for the four algorithms and the three feature combinations, using four, three or two seasons depending on their availability. Number of classifications: 180.

**Figure 8.**Main effects and simple effects in significant factors (mean ±). Means with different letters are significantly different (Tukey–Kramer contrast using HC3, alpha = 0.05). Number of classifications: 288.

**Figure 9.**Per class user’s average accuracy values (user’s and producer’s) using four season imagery. Number of classifications: 72. Sp: Spectral features; Tx: Textural features; Tr: Terrain features.

**Figure 10.**Four-season confusion matrix in 2015 using spectral and textural features (top) and spectral, textural and terrain features (bottom) with RF algorithm. The number of correctly classified pixels and the producer’s (left) and user’s (right) accuracy values appear in the diagonal. Outside the diagonal appear the number of confusions, the percentage of the column class incorrectly classified as the row class (left) and the percentage of the row class that truly belongs to the column class (right).

**Figure 11.**Four-season imagery classification in 2015 using spectral and textural features (

**top**) and spectral; textural and terrain features (

**bottom**) using RF algorithm.

Date | Sensor | Date | Sensor | Date | Sensor | Date | Sensor | Date | Sensor |
---|---|---|---|---|---|---|---|---|---|

2000 | 2001 | 2002 | 2003 | 2004 | |||||

29-Jan-00 | ETM+ | 1-Dec-01 | TM | 6-Feb-03 | ETM+ | 6-Feb-03 | ETM+ | 4-Mar-04 | TM |

21-Jun-00 | ETM+ | 21-Apr-01 | ETM+ | 24-Apr-02 | ETM+ | 10-March-03 | TM | 13-Apr-04 | ETM+ |

8-Aug-00 | ETM+ | 26-Jul-01 | ETM+ | 19-Jun-02 | TM | 29-May-03 | TM | 19-Aug-04 | ETM+ |

27-Oct-00 | ETM+ | 30-Oct-01 | ETM+ | - | - | 26-Sep-03 | TM | 15-Nov-04 | TM |

2005 | 2006 | 2007 | 2008 | 2009 | |||||

4-Mar-04 | TM | 24-Jan-07 | TM | 24-Jan-07 | TM | 14-Feb-09 | TM | 14-Feb-09 | TM |

18-May-05 | ETM+ | 6-Jun-06 | ETM+ | 8-May-07 | ETM+ | 19-Jun-08 | TM | 5-May-09 | TM |

26-Jun-05 | TM | 16-Jul-06 | TM | 4-Aug-07 | TM | 15-Sep-08 | ETM+ | 24-Jul-09 | TM |

12-Dec-05 | ETM+ | 13-Nov-06 | ETM+ | 16-Nov-07 | ETM+ | 1-Oct-08 | ETM | 10-Sep-09 | TM |

2010 | 2011 | 2013 | 2014 | 2015 | |||||

16-Nov-10 | TM | 4-Feb-11 | TM | - | - | 16-Mar-14 | OLI | 2-Feb-15 | OLI |

24-May-10 | TM | 9-Apr-11 | TM | 14-Apr-13 | OLI | 4-Jun-14 | OLI | 7-Jun-15 | OLI |

11-Jul-10 | TM | 28-Jun-11 | TM | 19-Jul-13 | OLI | 22-Jul-14 | OLI | 9-Jul-15 | OLI |

29-Sep-10 | TM | - | - | 14-Nov-13 | OLI | 26-Oct-14 | OLI | 30-Nov-15 | OLI |

**Table 2.**Number and area (ha) of training and validation areas. Water surfaces include some sea polygons. The percentages refer to the areas. Bare soil was initially included within the scrub class, but because of its different reflectivity, we have considered it as a new class.

Training Areas | Validation Areas | |||||
---|---|---|---|---|---|---|

Use | N | Area | % | N | Area | % |

Forest | 19 | 328.21 | 13.66 | 10 | 98.35 | 9.79 |

Scrub | 22 | 302.43 | 12.59 | 12 | 213.50 | 21.26 |

Rainfed tree crops | 13 | 77.89 | 3.24 | 7 | 51.39 | 5.12 |

Irrigated tree crops | 14 | 148.01 | 6.16 | 8 | 39.78 | 3.96 |

Rainfed grassland | 15 | 231.85 | 9.65 | 8 | 111.01 | 11.05 |

Irrigated grassland | 10 | 293.20 | 12.20 | 5 | 103.74 | 10.33 |

Impervious surfaces | 16 | 423.84 | 17.64 | 7 | 112.86 | 11.24 |

Water surfaces | 11 | 391.12 | 16.28 | 6 | 207.72 | 20.69 |

Bare soil | 4 | 7.56 | 0.31 | 2 | 8.02 | 0.80 |

Vineyard | 17 | 198.62 | 8.27 | 8 | 57.81 | 5.76 |

Total | 141 | 2402.73 | 100 | 73 | 1004.18 | 100 |

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## Share and Cite

**MDPI and ACS Style**

Gomariz-Castillo, F.; Alonso-Sarría, F.; Cánovas-García, F.
Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015. *Remote Sens.* **2017**, *9*, 1058.
https://doi.org/10.3390/rs9101058

**AMA Style**

Gomariz-Castillo F, Alonso-Sarría F, Cánovas-García F.
Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015. *Remote Sensing*. 2017; 9(10):1058.
https://doi.org/10.3390/rs9101058

**Chicago/Turabian Style**

Gomariz-Castillo, Francisco, Francisco Alonso-Sarría, and Fulgencio Cánovas-García.
2017. "Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015" *Remote Sensing* 9, no. 10: 1058.
https://doi.org/10.3390/rs9101058