Multiple Kernel Graph Cut for SAR Image Change Detection

Lu Jia,Jing Fang,Tiantian Zhang,Feibiao Dong

doi:10.3390/rs13040725

Lu Jia, Jing Fang + Show 2 more

Open Access

https://doi.org/10.3390/rs13040725

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Journal: Remote Sensing	Publication Date: Feb 17, 2021
Citations: 7	License type: CC BY 4.0

Affiliation: Hefei University of Technology

Abstract

Complementary information between two difference images (DI’s) has great contribution to improve change detection performances. Based on the effectiveness and flexibility of the multiple kernel learning (MKL) in information fusion, we develop a multiple kernel graph cut (MKGC) algorithm for synthetic aperture radar (SAR) image change detection. An energy function containing a weighted summation kernel is proposed for fusing the complementary information between the subtraction image and the ratio image. By iteratively minimizing the energy function, the kernel weights, region parameters and region labels are estimated automatically and optimally. Besides of it avoids modeling, MKGC also has a complete description of the changed areas and the strong noise immunity. Experiments on real GaoFen-3 SAR data set demonstrate the effectiveness of the MKGC algorithm, and illustrate that it is a good candidate for SAR image change detection.

Highlights

Change detection aims at identifying changes in images of the same scene taken at different times [1]
The proposed multiple kernel graph cut (MKGC) algorithm is compared with four methods
They are the ratio kernel-based support vector machine (SVM) [15], the CNN [17] acting on the ratio images, and the subtraction and ratio images classified by the kernel graph cut (KGC) [20]

Summary

Introduction

Change detection aims at identifying changes in images of the same scene taken at different times [1]. It is a vital branch of remote sensing image interpretation, and it is attracting a growing interest in civil and military applications, such as environment monitoring, disaster prevention and relief, urban study and so on [2,3,4,5]. SAR image change detection techniques, which can comprehensively detect changed areas as well as resisting speckle noise and background disturbances, still face technical challenges. It. Itspace, is more more general for image image the image from the classification regions the kernel while the smoothness classification by avoiding seeking the accurate image models. The data term measures the deviation of consists of a data term and a smoothness term

Methods

Results

Discussion

Conclusion