A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images

Hao, Ming; Shi, Wenzhong; Hua, Zhang; Wang, Qunming; Deng, Kazhong

doi:10.3390/rs8090745

Cited by 11 publications

(6 citation statements)

References 42 publications

(46 reference statements)

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“…This means that the noise removal process consists of thresholding noisy wavelet coefficients in the image at different image restoration levels [98], [99]. The application of this algorithm enables to enhance the spatial resolution of the image, which, in turn, improves the level of detail, and, as a consequence, the interpretation ability of the image, which was ultimately reflected in numerous studies on monitoring the environment [100]- [102], detecting changes [103]- [105], and mapping [106]- [108].…”

Section: Other Digital Image Processing Methods To Improve Image Reso...mentioning

confidence: 99%

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Karwowska

Wierzbicki

2022

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

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Section: Other Digital Image Processing Methods To Improve Image Reso...mentioning

confidence: 99%

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Karwowska

Wierzbicki

2022

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

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“…With the development of various optical satellite sensors capable of acquiring very high resolution (VHR) images, the images have been used in a wide range of applications in the remote sensing field. different data sources [37,38]. Thus, the fusion of CD results using DST aims to improve the reliability of decisions by taking advantage of complementary information while decreasing their uncertainty [39].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

et al. 2020

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Change detection (CD), one of the primary applications of multi-temporal satellite images, is the process of identifying changes in the Earth’s surface occurring over a period of time using images of the same geographic area on different dates. CD is divided into pixel-based change detection (PBCD) and object-based change detection (OBCD). Although PBCD is more popular due to its simple algorithms and relatively easy quantitative analysis, applying this method in very high resolution (VHR) images often results in misdetection or noise. Because of this, researchers have focused on extending the PBCD results to the OBCD map in VHR images. In this paper, we present a proposed weighted Dempster-Shafer theory (wDST) fusion method to generate the OBCD by combining multiple PBCD results. The proposed wDST approach automatically calculates and assigns a certainty weight for each object of the PBCD result while considering the stability of the object. Moreover, the proposed wDST method can minimize the tendency of the number of changed objects to decrease or increase based on the ratio of changed pixels to the total pixels in the image when the PBCD result is extended to the OBCD result. First, we performed co-registration between the VHR multitemporal images to minimize the geometric dissimilarity. Then, we conducted the image segmentation of the co-registered pair of multitemporal VHR imagery. Three change intensity images were generated using change vector analysis (CVA), iteratively reweighted-multivariate alteration detection (IRMAD), and principal component analysis (PCA). These three intensity images were exploited to generate different binary PBCD maps, after which the maps were fused with the segmented image using the wDST to generate the OBCD map. Finally, the accuracy of the proposed CD technique was assessed by using a manually digitized map. Two VHR multitemporal datasets were used to test the proposed approach. Experimental results confirmed the superiority of the proposed method by comparing the existing PBCD methods and the OBCD method using the majority voting technique.

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“…Dempster-Shafer Theory (D-S) is a decision theory effectively fusing multiple evidences from different sources [41]. One important advantage of D-S theory is that it can provide explicit estimations of uncertainty between different evidences from different data sources [42,43]. Thus, the fusion of change detection results by D-S theory aims to improve the reliability of decisions by taking advantage of complementary information while decreasing their uncertainty [44].…”

Section: Introductionmentioning

confidence: 99%

Urban Change Detection Based on Dempster–Shafer Theory for Multitemporal Very High-Resolution Imagery

Luo

Liu

et al. 2018

Remote Sensing

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Fusing multiple change detection results has great potentials in dealing with the spectral variability in multitemporal very high-resolution (VHR) remote sensing images. However, it is difficult to solve the problem of uncertainty, which mainly includes the inaccuracy of each candidate change map and the conflicts between different results. Dempster-Shafer theory (D-S) is an effective method to model uncertainties and combine multiple evidences. Therefore, in this paper, we proposed an urban change detection method for VHR images by fusing multiple change detection methods with D-S evidence theory. Change vector analysis (CVA), iteratively reweighted multivariate alteration detection (IRMAD), and iterative slow feature analysis (ISFA) were utilized to obtain the candidate change maps. The final change detection result is generated by fusing the three evidences with D-S evidence theory and a segmentation object map. The experiment indicates that the proposed method can obtain the best performance in detection rate, false alarm rate, and comprehensive indicators.

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A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images

Cited by 11 publications

References 42 publications

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster–Shafer Theory

Urban Change Detection Based on Dempster–Shafer Theory for Multitemporal Very High-Resolution Imagery

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