Deep Canonical Correlation Analysis Network for Scene Change Detection of Multi-Temporal VHR Imagery

2019 ◽  
Author(s):  
Lixiang Ru ◽  
Chen Wu ◽  
Bo Du ◽  
Liangpei Zhang
Keyword(s):  
Correlation Analysis ◽  
Change Detection ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Scene Change ◽  
Scene Change Detection ◽  
Multi Temporal

scholarly journals KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION

2016 ◽  
Vol III-1 ◽  
pp. 49-53 ◽  
Author(s):  
Yang Bai ◽  
Ping Tang ◽  
Changmiao Hu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Landsat 8 ◽  
Linear Relationships ◽  
Multi Temporal ◽  
Kernel Canonical Correlation Analysis ◽  
The Difference ◽  
Complex Relationships ◽  
Beijing China

The multivariate alteration detection (MAD) algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA) which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA). The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1) data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.

scholarly journals KERNEL MAD ALGORITHM FOR RELATIVE RADIOMETRIC NORMALIZATION

2016 ◽  
Vol III-1 ◽  
pp. 49-53
Author(s):  
Yang Bai ◽  
Ping Tang ◽  
Changmiao Hu
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Landsat 8 ◽  
Linear Relationships ◽  
Multi Temporal ◽  
Kernel Canonical Correlation Analysis ◽  
The Difference ◽  
Complex Relationships ◽  
Beijing China

The multivariate alteration detection (MAD) algorithm is commonly used in relative radiometric normalization. This algorithm is based on linear canonical correlation analysis (CCA) which can analyze only linear relationships among bands. Therefore, we first introduce a new version of MAD in this study based on the established method known as kernel canonical correlation analysis (KCCA). The proposed method effectively extracts the non-linear and complex relationships among variables. We then conduct relative radiometric normalization experiments on both the linear CCA and KCCA version of the MAD algorithm with the use of Landsat-8 data of Beijing, China, and Gaofen-1(GF-1) data derived from South China. Finally, we analyze the difference between the two methods. Results show that the KCCA-based MAD can be satisfactorily applied to relative radiometric normalization, this algorithm can well describe the nonlinear relationship between multi-temporal images. This work is the first attempt to apply a KCCA-based MAD algorithm to relative radiometric normalization.

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