Unsupervised Change Detection of Remote Sensing Images Using Superpixel Segmentation and Variational Gaussian Mixture Model

Gang Yang, Heng-Chao Li, and Chi Liu Presented by Chi Liu

Sichuan Provincial Key Laboratory of Information Coding and Transmission

School of Information and Science Technology Southwest Jiaotong University

1. Introduction 2. Proposed Method

3. Experimental Results

4. Conclusion

Outline

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3

Introduction

a) Time 1, 𝑌𝑌1

b) Time 2 , 𝑌𝑌2

c) Ground Truth

Similarity in neighboring pixels, which can improve the change detection result.

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4

Introduction

Generate Difference Map,

𝑌𝑌𝑑𝑑 a) Time 1, 𝑌𝑌1

b) Time 2 , 𝑌𝑌2

Model the Difference Map

Classify the pixel into two

categories (i.e., changed and unchanged

classes)

It is important to accurately model the difference map for the good change detection results in the following step.

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1. Introduction

2. Proposed Method 3. Experimental Results

4. Conclusion

Outline

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6

Generation of the difference image

Superpixel segmentation

Variational GMM

Decision based on MSE criterion

Change mask

Input image: Y1 Input image: Y2

(time t1 ) (time t2 )

Proposed Method

• For optical images, 𝑌𝑌𝑑𝑑 = |𝑌𝑌1 − 𝑌𝑌2|

• For SAR images: 𝑌𝑌𝑑𝑑 = |log𝑌𝑌1 − logY2|

• Normalization : 𝑌𝑌𝑑𝑑 = 𝑌𝑌𝑑𝑑−min (𝑌𝑌𝑑𝑑)

max 𝑌𝑌𝑑𝑑 −min (𝑌𝑌𝑑𝑑)

SAR: Synthetic aperture radar /18

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Generation of the difference image

Superpixel segmentation

Variational GMM

Decision based on MSE criterion

Change mask

Input image: Y1 Input image: Y2

(time t1 ) (time t2 )

Proposed Method

• Increase the speed • Take the similarity in the neighboring pixels

into account.

• A superpixel is represented by the average of it pixels

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Generation of the difference image

Superpixel segmentation

Variational GMM

Decision based on MSE criterion

Change mask

Input image: Y1 Input image: Y2

(time t1 ) (time t2 )

Proposed Method

• Gaussian mixture model (GMM) is quite flexible for statistically modeling task and has been pervasively used.

• Gaussian mixture model (GMM) is a weighted combination of Gaussian distributions.

• GMM is used to model the resulting superpixels in our method.

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Generation of the difference image

Superpixel segmentation

Variational GMM

Decision based on MSE criterion

Change mask

Input image: Y1 Input image: Y2

(time t1 ) (time t2 )

Proposed Method

• With this inference method, some of the unnecessary Gaussian distribution can be eliminated automatically if the initial number of Gaussian distribution in GMM is larger than needed.

• Variational inference is used in our method to estimate the GMM.

• Classify each superpixel according to the resulting GMM.

Generally, there could be more than two classes!!! /18

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Generation of the difference image

Superpixel segmentation

Variational GMM

Decision based on MSE criterion

Change mask

Input image: Y1 Input image: Y2

(time t1 ) (time t2 )

Proposed Method

st cluster nd cluster th cluster...

Changed pixels Unchanged pixels

min𝑤𝑤𝑐𝑐,𝑤𝑤𝑢𝑢

𝑀𝑀𝑀𝑀𝑀𝑀 = �𝐶𝐶(𝑤𝑤_𝑣𝑣)𝐼𝐼 × 𝐽𝐽

𝑣𝑣={𝑐𝑐,𝑢𝑢}

𝑀𝑀𝑊𝑊𝑣𝑣

𝑀𝑀𝑊𝑊𝑣𝑣 =1

𝐶𝐶(𝑤𝑤𝑣𝑣)� 𝑥𝑥𝑛𝑛 − 𝜙𝜙𝑣𝑣 2

∀𝑥𝑥𝑛𝑛∈𝑤𝑤𝑣𝑣

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1. Introduction

2. Proposed Method

3. Experimental Results 4. Conclusion

Outline

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Experimental Results Data sets:

• Optical images • August 5, 1986 and August 5, 1992 • Parts of the Reno-Lake Tahoe area (U.S.) • 200×200

• Synthetic aperture radar (SAR) images • April and May 1999 • near the city of Bern, Switzerland • 301×301

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Experimental Results Experimental Results of SAR Images:

c) ground truth mask

d) EM e) Bayes-GMM f) Proposed

a) Time 1 b) Time 2

• Less miscellaneous pixels • Smoother • Quite similar to the ground truth • Good for visual interpretation

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Experimental Results Experimental Results of Optical Images:

c) ground truth mask

d) EM e) Bayes-GMM f) Proposed

a) Time 1 b) Time 2

• Smoother • Good for visual interpretation • Some changed pixels are

missed • Further quantitative analysis

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Experimental Results Experimental Results:

• The proposed method can provide better change detection results according to the lower values of total error.

Optical images SAR images False Alarm,

%

Missed Detection,

%

Total Error,

%

False Alarm,

%

Missed Detection,

%

Total Error,

% EM-based 4.20 7.72 4.41 3.36 6.32 3.40

Bayes-GMM 0.18 52.73 3.28 0.73 18.61 0.96 Proposed 0.48 45.87 3.16 0.19 24.42 0.50

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1. Introduction

2. Proposed Method

3. Experimental Results

4. Conclusion

Outline

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Conclusion

Effective method in change detection.

Applicable for both the optical and SAR images.

Smooth change mask with good visual interpretation.

To improve the missed detection. Future work

Conclusion

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Thanks for your attention !

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