#
Probability Estimation of Change Maps Using Spectral Similarity^{ †}

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^{†}

## Abstract

**:**

## 1. Introduction

## 2. Experiments

#### 2.1. Study Area and Data Set

#### 2.2. Adopted Methodology

**Figure 2.**The flowchart of the proposed approach to produce probability map of changes. PCA: principal component analysis; ICA: independent component analysis; CE: cross equalization; MLE: maximum likelihood estimator; ED: Euclidean distance.

#### 2.2.1. Step 1

#### 2.2.2. Step 2

#### 2.2.3. Step 3

_{1}, y is the spectral signature vector of a pixel (in the abundance maps of endmembers) in the image of time t

_{2}, and n is the number of endmembers. The spectral angle goes from 0 when signatures are identical to 90 when signatures are completely different.

#### 2.2.4. Step 4

## 3. Results and Discussion

## 4. Conclusions

## Author Contributions

## Conflicts of Interest

## Abbreviations

## References

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**Figure 1.**False color composites of the original hyperspectral images acquired in (

**a**) 2004 and (

**b**) 2007 of the USA data set, and (

**c**) binary change map ground truth.

**Figure 3.**Result of the performance of binary change detection methods in USA data set. (

**a**) PCA, (

**b**) ICA, (

**c**) CE, (

**d**) MLE, (

**e**) ED, and (

**f**) final BCM.

**Figure 4.**Results of the estimated probability maps of employed spectral similarity metrics. (

**a**) SAM, (

**b**) PCC, (

**c**) BCD, (

**d**) JMD, (

**e**) final probability map of changes, and (

**f**) legend.

**Table 1.**Numerical analysis of binary change detection methods. OA: overall accuracy; κ: kappa; FPR: false positive rate; MCC: Matthews correlation coefficient.

Method | OA (%) | κ | FPR | MCC |
---|---|---|---|---|

PCA | 96.20 | 0.8835 | 0.0583 | 0.8845 |

ICA | 88.68 | 0.6504 | 0.2425 | 0.6516 |

CE | 96.54 | 0.8909 | 0.0672 | 0.8912 |

MLE | 87.83 | 0.5874 | 0.3856 | 0.5900 |

ED | 96.15 | 0.8852 | 0 | 0.8911 |

Final BCM | 97.00 | 0.9055 | 0.0527 | 0.9059 |

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**MDPI and ACS Style**

Jafarzadeh, H.; Hasanlou, M.
Probability Estimation of Change Maps Using Spectral Similarity. *Proceedings* **2019**, *18*, 8.
https://doi.org/10.3390/ECRS-3-06183

**AMA Style**

Jafarzadeh H, Hasanlou M.
Probability Estimation of Change Maps Using Spectral Similarity. *Proceedings*. 2019; 18(1):8.
https://doi.org/10.3390/ECRS-3-06183

**Chicago/Turabian Style**

Jafarzadeh, Hamid, and Mahdi Hasanlou.
2019. "Probability Estimation of Change Maps Using Spectral Similarity" *Proceedings* 18, no. 1: 8.
https://doi.org/10.3390/ECRS-3-06183