The regularized iteratively reweighted object-based MAD method for change detection in bi-temporal, multispectral data

Xu, Qiangqiang; Liu, Zhengjun; Li, Fangfang; Yang, Mingze; Ren, H C

doi:10.1117/12.2245323

Cited by 5 publications

(15 citation statements)

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“…Currently, a lot of change detection methods have been reported to detect the changed information on this earth we live. These change detection methods can be roughly grouped into three categories: pixel-based approaches [8][9][10][11][12][13][14][15][16][17][18][19], objectbased approaches [20][21][22][23][24][25], and deep learning (DL) based approaches [26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…The pixel-based approaches include thresholding-based methods [8][9][10], clustering-based methods [11][12][13][14][15], expectation maximinzation (EM) based methods [16][17], and multivariate alteration detection (MAD) based methods [18][19]. The basic idea of thresholding is simple as it calculates the threshold of a difference image according to the grayscale distribution of pixels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

Xue

Lei

Jia

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

Xue

Lei

Jia

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Remote sensing image change detection technology has been applied in many fields, such as environmental monitoring (Zhuang, Deng, & Fan, 2016), urban research (Zhuang, Deng, Yu, & Fan, 2017), land use (Yonezawa, 2007), sand cover monitoring (Yan-Hong, Pei, Wang, & Yun-Peng, 2010), forest monitoring (Zhuang, Deng, Fan, & Ma, 2018), agricultural investigation (Shi, Gao, & Shen, 2016), and disaster assessment (Chen & Chen, 2016). The change detection of remote sensing images is based on the multiple remote sensing images acquired at different time points in the same region to extract the features and process the changes in the ground objects.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the IR-MAD algorithm fails to make full use of the variation characteristics of each band, resulting in the incomplete detection of the details of changed areas. Therefore, the algorithm has broken patches, much noise, and small change areas that are difficult to detect, and the overall detection rate is low (Xu, Liu, Li, Ren, & Yang, 2016). In recent years, with the development of machine learning methods, neural networks have also been applied to change detection in multi-spectral images.…”

Section: Introductionmentioning

confidence: 99%

Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering

Jia

Yang

et al. 2019

European Journal of Remote Sensing

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In the present study, an improved iteratively reweighted multivariate alteration detection (IR-MAD) algorithm was proposed to improve the contribution of weakly correlated bands in multi-spectral image change detection. In the proposed algorithm, each image band was given a different weight through single-band iterative weighting, improving the correlation between each pair of bands. This method was used to obtain the characteristic difference in the diagrams of the band that contain more variation information. After removing Gaussian noise from each feature-difference graph, the difference graphs of each band were fused into a change-intensity graph using the Euclidean distance formula. Finally, unsupervised fuzzy C-means (FCM) clustering was used to perform binary clustering on the fused difference graphs to obtain the change detection results. By comparing the original multivariate alteration detection (MAD) algorithm, the IR-MAD algorithm and the proposed IR-MAD algorithm, which used a mask to eliminate strong changes, the experimental results revealed that the multi-spectral change detection results of the proposed algorithm are closer to the actual value and had higher detection accuracy than the other algorithms.

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“…Therefore, the IR-MAD algorithm fails to make full use of the variation characteristics of each wave band, resulting in the incomplete detection of the variation areas of details. Therefore, the algorithm has broken patches, much noise, and small change areas that are difficult to detect, and the overall detection rate is low [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Spectral Image Change Detection Based on Band Selection and Single-Band Iterative Weighting

Jia

et al. 2019

IEEE Access

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Iteratively reweighted multivariate alteration detection algorithm has the phenomena of broken patches, much noise, and small change area that are difficult to detect, and the overall detection rate is low. In order to solve this problem, this paper proposes a multi-spectral image change detection algorithm based on band selection and single-band iterative weighting. Because the change information of the multi-spectral image is concentrated in some bands, the background and noise information of the rest bands are more, which may have a negative effect on the final result. Therefore, the band with more change information is selected first, and the iterative weighting of a single band can better suppress the noise and background information, so as to obtain a higher band correlation and facilitate the extraction of change information. This method is used to obtain the characteristic difference graph of the selected band with more change information. After Gaussian denoising of each characteristic difference graph, the Euclidean distance formula is used to fuse the difference graph of each band into a change intensity graph. Finally, the unsupervised k-means clustering algorithm is used to perform binary-valued clustering on the fused difference graph to obtain the change detection results. As a practical application, the superior performance of our proposed method was demonstrated through a large number of comparative tests. INDEX TERMS Multi-spectral change detection, IR-MAD, band selection.

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The regularized iteratively reweighted object-based MAD method for change detection in bi-temporal, multispectral data

Cited by 5 publications

References 1 publication

Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering

Multi-Spectral Image Change Detection Based on Band Selection and Single-Band Iterative Weighting

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