Remote Sensing Letters
ISSN: 2150-704X (Print) 2150-7058 (Online) Journal homepage: http://www.tandfonline.com/loi/trsl20
Change detection in multispectral images based on multiband structural information Huifu Zhuang, Zhixiang Tan, Kazhong Deng & Guobiao Yao To cite this article: Huifu Zhuang, Zhixiang Tan, Kazhong Deng & Guobiao Yao (2018) Change detection in multispectral images based on multiband structural information, Remote Sensing Letters, 9:12, 1167-1176 To link to this article: https://doi.org/10.1080/2150704X.2018.1516310
Published online: 05 Oct 2018.
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REMOTE SENSING LETTERS 2018, VOL. 9, NO. 12, 1167–1176 https://doi.org/10.1080/2150704X.2018.1516310
Change detection in multispectral images based on multiband structural information Huifu Zhuanga, Zhixiang Tana, Kazhong Dengb and Guobiao Yaoc a
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou, China; bSchool of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, China; cSchool of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, China ABSTRACT
ARTICLE HISTORY
Change vector analysis (CVA) and spectral angle mapper (SAM) are usually used to generate difference image in change detection of multispectral images. Although CVA and SAM can describe the difference between multispectral images, they are defined mathematically and lack support of human visual system (HVS) theory. Advanced structural similarity (ASSIM) complies with the pattern that human perceives the changes occurred in an objective scene. Nevertheless, ASSIM was designed for single band images and cannot be used for extracting multiband structural information from multispectral images directly. Therefore, we first propose two strategies to extract multiband structural information from multiband images. Then, we propose the approaches based on multiband structural information for change detection in multispectral images. Experimental results from one semisynthetic data set and two real data sets acquired by Sentinel-2A and QuickBird satellites validate the effectiveness of the proposed approaches.
Received 1 April 2018 Accepted 22 August 2018
1. Introduction Change detection techniques can be used to detect the land cover changes that have occurred in the studied area by utilizing remote sensing images acquired at two different times (Bruzzone and Prieto 2000). In recent years, they have been widely used in environmental monitoring (Onur et al. 2009), urban studies (Ye and Chen 2015), forest monitoring (Chehata et al. 2014), agricultural surveys (Li and Narayanan 2003), disaster assessment (D’Addabbo et al. 2016), etc. Change detection is usually divided into three main sequential steps (Lorenzo and Diego Fernàndez 2002; Su, Gong, and Sun 2014): 1) image preprocessing; 2) generating a difference image by comparing multitemporal images; and 3) analysis of the difference image. Among them, generating the difference image is a key step to carry out change detection. Change vector analysis (CVA) (Malila 1980) is a classic method to generate the difference image and is widely used in change detection of multispectral images (Bruzzone and Prieto 2000; Lorenzo and Diego Fernàndez 2002; Onur et al. 2009; CONTACT Huifu Zhuang
[email protected] Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, 221116 Xuzhou, China © 2018 Informa UK Limited, trading as Taylor & Francis Group
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ZZhuang et al. 2016). CVA can also be used in the areas of multiclass change detection (Liu et al. 2017), surface water change detection (Huang et al. 2016), and forecasting natural hazards (Singh et al. 2017), etc. In the literature (Moughal and Yu 2014), spectral angle mapper (SAM) for change detection in Landsat-5 TM images was studied in view of the fact that SAM was not taken into account in most literature focusing on change detection of multispectral images. CVA utilizes the magnitude of change vector to describe the difference between multitemporal multispectral images, while SAM presents the difference by spectral angle (i.e., the angle of the two spectral vectors, for images with n bands, the spectral vector is composed of the pixels of different bands at the same position). The changed pixel characterizes possessing the larger magnitude/ larger angle. Preserving changed pixels (PCPs) (Júnior et al. 2011) and autoadapted fusion strategy (AFS) (ZZhuang et al. 2016) were reported to integrating the advantages of CVA and SAM. CVA and SAM are mathematically defined difference measure, they describe the difference from the perspective of vector magnitude and direction, respectively. Although the methods such as CVA, SAM, PCPs, and AFS have good performance in change detection of multispectral images, they lack the support of human visual system (HVS) theory. The methods based on HVS theory comply with the pattern that human perceives the changes occurred in an objective scene and are expected to have better performance in change detection (Zhuang et al. 2018). Structural information, extracted by simulating HVS, is independent of viewing conditions and individual observers (Wang and Bovik 2002). Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, advanced structural similarity (ASSIM) was proposed for change detection in synthetic aperture radar images (Zhuang et al. 2018). However, ASSIM cannot be applied in change detection of multispectral images directly because it was designed for extracting structural information from two images with single band. Although we can apply ASSIM to the PC1 band (named PC1-ASSIM) from principal component analysis (PCA), the PC1 band loses the main feature of multispectral image (i.e. describing a pixel with multiband spectral information). To overcome this limit, two strategies for extracting multiband ASSIM (MASSIM) from multiband images are proposed in this study, which can extract multiband structural information from the images with n bands and are simplified to ASSIM when n ¼ 1. Then we propose the methods on the basis of MASSIM for change detection of multispectral images.
2. Methodology 2.1. ASSIM ASSIM assumes that the process of extracting structural information by HVS can be divided into two steps (Zhuang et al. 2018). First, HVS acquires the signal intensity, color, position, and other information through the human eye. The signal intensities on different positions acquired by HVS constitute the HVS signal space. Second, ASSIM, aided by the human brain, is extracted from the information acquired in the first step. The value of visual attention measure (VAM) can reflect the importance of the signal at a certain position observed by HVS (Zhuang et al. 2018). The larger the VAM, the greater the importance. For a neighborhood, the VAM values on different positions are
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estimated by the two-dimensional Gaussian distance function shown in (1), and the sum of VAM is normalized to 1. ! ðx pÞ2 ðy qÞ2 Gðx; yÞ ¼ exp (1) 2σ21 2σ22 where ðx; yÞ are the coordinates of a signal; ðp; qÞ are the coordinates of the center signal; σ1 and σ2 are the standard deviations of the Gaussian kernel functions in the horizontal and vertical directions, respectively. For the isotropic Gaussian distance function, σ1 ¼ σ2 . VAM intensity (VAMI) is defined as the product between VAM and the true signal intensity. The VAM signal space is comprised of VAMI on different positions. Let X ¼ fxi ji ¼ 1; 2; :::; Ng and Y ¼ fyi ji ¼ 1; 2; :::; Ng represent the original and test image signals, respectively. Let X 0 ¼ fx0 i ji ¼ 1; 2; :::; Ng and y0 ¼fy0 i ji ¼ 1; 2; :::; Ng represent the original and test image signals in VAM signal space. ASSIM was designed by modeling any image distortion as a combination of luminance, contrast, and structure distortions, which can be computed by Equation (2) " #α " #β 2ux0 uy0 þ C1 2σx0 σy0 þ C2 σ x 0 y 0 þ C3 γ 0 0 ASSIMðx ; y Þ ¼ 2 (2) σ x 0 σ y 0 þ C3 ux0 þ u2y0 þ C1 σ2x0 þ σ2y0 þ C2 where α > 0, β > 0, γ > 0 are parameters used to adjust the relative importance of the three components; ux0 and uy0 are the means of x0 and y0 ; X0 and σy0 are the unbiased estimators of the standard variance of σx0 y0 and y0 ; σx0 y0 is the unbiased estimator of the covariance of x0 and y0 ; C1 , C2 , and C3 are constants, which can guarantee the numerical stability while the denominator is close to zero. The constants C1 , C2 , and C3 are computed with C ¼ ðkLÞ2 , where L is the dynamic range of the pixel values (L ¼ 255 for 8-bit grayscale images), k
