Change detection from remotely sensed images: From pixel-based to object-based approaches

Hussain, Masroor; Chen, Dongmei; Cheng, Angela; Wei, Hui; Stanley, David

doi:10.1016/j.isprsjprs.2013.03.006

Cited by 1,261 publications

(818 citation statements)

References 169 publications

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“…Imagery with higher spatial resolutions is needed to quantify such changes at very fine scales. Change detection based on very high spatial resolution data, however, could be very challenging, particularly when using pixel-based approaches (Hussain et al, 2013). For example, it is difficult to address the abundant false changes due to spatial misregistration .…”

Section: Discussionmentioning

confidence: 99%

“…Various methods have been developed for land cover change analysis using remotely sensed data (Coppin, Jonckheere, Nackaerts, Muys, & Lambin, 2004;Hussain, Chen, Cheng, Wei, & Stanley, 2013;Lu, Mausel, Brondizio, & Moran, 2004;Tewkesbury, Comber, Tate, Lamb, & Fisher, 2015). These methods may be broadly classified into two categories: post-classification comparison and pre-classification change detection (Lu et al, 2004;Singh, 1989;Zhou, Troy, & Grove, 2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Zhou

Qian

et al. 2016

Remote Sensing of Environment

155

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Zhou

Qian

et al. 2016

Remote Sensing of Environment

155

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“…To evaluate and address suitably the effect of changes in land coverage and land use, several studies and models of change detection based on multispectral image data analysis have been carried out such as principal component analysis (PCA), image subtraction, image ratioing, change vector analysis, image regression, and spectral features variance, which are the usual methods of the change detection assessment [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Thresholding Algorithm Optimization for Change Detection to Satellite Imagery

Vázquez-Jiménez¹,

Ramos-Bernal²,

Calcerrada³

et al. 2018

Colorimetry and Image Processing

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To detect changes in satellite imagery, a supervised change detection technique was applied to Landsat images from an area in the south of México. At first, the linear regression (LR) method using the first principal component (1-PC) data, the Chi-square transformation (CST) method using first three principal component (PC-3), and tasseled cap (TC) images were applied to obtain the continuous images of change. Then, the threshold was defined by statistical parameters, and histogram secant techniques to categorize as change or unchanged the pixels. A threshold optimization iterative algorithm is proposed, based on the ground truth data and assessing the accuracy of a range of threshold values through the corresponding Kappa coefficient of concordance.Finally, to evaluate the change detection accuracy of conventional methods and the threshold optimization algorithm, 90 polygons (15,543 pixels) were sampled, categorized as real change/unchanged zones, and defined as ground truth, from the interpretation of color aerial photo slides aided by the land cover maps to obtain the omission/ commission errors and the Kappa coefficient of agreement. The results show that the threshold optimization is a suitable approach that can be applied for change detection analysis.

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“…However, thus far, spatial information is used in common practice, only in high resolution (HR) or very high resolution (VHR) images, although it is gaining popularity in image segmentation and change detection [14][15][16]. Several objects based change detection techniques which considered spatial information were discussed in [16]. Most of the segmentation approaches were grouped into either boundary or edge-based (discontinuity of pixels) or area-based (similarity of pixels) techniques.…”

Section: Introductionmentioning

confidence: 99%

Coupling of Thresholding and Region Growing Algorithm for Change Detection in Sar Images

Mishra¹,

Susaki²

2013

PIER

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Abstract-In this research paper, we propose supervised and unsupervised change detection methodologies focused on the analysis of multitemporal Synthetic Aperture Radar (SAR) images. These approaches are based on three main steps: (1) a comparison of multitemporal image was carried out by normalized difference ratio (NDR) operator; (2) implementing a novel supervised or unsupervised thresholding and (3) generating the change map by coupling of thresholding along with a region growing algorithm. In the first step, two filtered multitemporal images were used to generate NDR image that was subjected to analysis. In the second step, by assuming a Gaussian distribution in the no-change area, we identified the pixel range that fits the Gaussian distribution better than any other range iteratively to detect the no-change area that eventually separates the change areas. In the supervised method, several sample no-change pixels were selected and the mean (μ) and the standard deviation (σ) were obtained. Then, μ ± 3σ was applied to select the best threshold values. Finally, a traditional thresholding algorithm was modified and implemented with the coupling of the region growing algorithm to consider the spatial information to generate the change map. The Gaussian distribution was assumed because it better fits the conditional densities of the no-change class in the NDR image. The effectiveness of the proposed methods was verified with the simulated images and the real images associated with geographical locations. The results were compared with the manual trial and error procedure (MTEP) and traditional unsupervised expectation-maximization (EM) method. Both proposed methods gave similar results with MTEP and significant improvement in Kappa coefficient in comparison to the traditional EM method was found in both geographical locations. 520 Mishra and SusakiThe coupling of the modified thresholding with the region growing algorithm is very effective with all methods.

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Change detection from remotely sensed images: From pixel-based to object-based approaches

Cited by 1,261 publications

References 169 publications

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

A new approach for land cover classification and change analysis: Integrating backdating and an object-based method

Thresholding Algorithm Optimization for Change Detection to Satellite Imagery

Coupling of Thresholding and Region Growing Algorithm for Change Detection in Sar Images

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