Robust unsupervised small area change detection from SAR imagery using deep learning
[Display omitted] •A multiscale superpixel reconstruction method was developed to generate the Difference Image.•A two-stage center-constrained FCM algorithm was designed to deal with imbalanced data clustering.•CWNN combined with DCGAN was adopted to classify hard pixels. Small area change detectio...
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| Published in: | ISPRS journal of photogrammetry and remote sensing Vol. 173; pp. 79 - 94 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.03.2021
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| Subjects: | |
| ISSN: | 0924-2716, 1872-8235 |
| Online Access: | Get full text |
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| Abstract | [Display omitted]
•A multiscale superpixel reconstruction method was developed to generate the Difference Image.•A two-stage center-constrained FCM algorithm was designed to deal with imbalanced data clustering.•CWNN combined with DCGAN was adopted to classify hard pixels.
Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection. |
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| AbstractList | [Display omitted]
•A multiscale superpixel reconstruction method was developed to generate the Difference Image.•A two-stage center-constrained FCM algorithm was designed to deal with imbalanced data clustering.•CWNN combined with DCGAN was adopted to classify hard pixels.
Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection. Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection. |
| Author | Zhang, Xinzheng Zhang, Ce Gu, Xiaowei Tan, Xiaoheng Su, Hang Atkinson, Peter M. |
| Author_xml | – sequence: 1 givenname: Xinzheng surname: Zhang fullname: Zhang, Xinzheng email: zhangxinzheng03@126.com organization: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China – sequence: 2 givenname: Hang surname: Su fullname: Su, Hang organization: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China – sequence: 3 givenname: Ce surname: Zhang fullname: Zhang, Ce email: c.zhang9@lancaster.ac.uk organization: Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom – sequence: 4 givenname: Xiaowei surname: Gu fullname: Gu, Xiaowei organization: Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, United Kingdom – sequence: 5 givenname: Xiaoheng surname: Tan fullname: Tan, Xiaoheng organization: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China – sequence: 6 givenname: Peter M. surname: Atkinson fullname: Atkinson, Peter M. organization: Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, United Kingdom |
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| Keywords | Deep learning Fuzzy c-means algorithm Change detection Synthetic aperture radar Difference image |
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•A multiscale superpixel reconstruction method was developed to generate the Difference Image.•A two-stage center-constrained FCM algorithm... Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes... |
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| SubjectTerms | algorithms Change detection data collection Deep learning Difference image Fuzzy c-means algorithm photogrammetry Synthetic aperture radar wavelet |
| Title | Robust unsupervised small area change detection from SAR imagery using deep learning |
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