Contextual Analysis Based Approach for Detecting Change from High Resolution Satellite Imagery

Lv, Zhiyong; Zhang, Wenyuan

doi:10.1007/s12524-017-0680-z

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…The most popular methods for generating a CMI are image difference [22], [25], image ratios [24], and change vector analysis [26], [27]. Furthermore, several notable derived methods for generating CMI have drawn attention; for example, Chen et al [28] proposed a spectral gradient difference for measuring the change magnitude between bitemporal multi-spectral remote sensing images; moreover, Lv and Zhang [20] promoted a change magnitude for very high resolution (VHR) remote sensing images on the basis of adaptive contextual information. In addition to generating a binary detection map, a binary threshold is required to obtain the final change inventory map.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to generating a binary detection map, a binary threshold is required to obtain the final change inventory map. The most frequently used binary threshold-determining methods are Otsu's method [20], [29] and expectation maximization [30]. However, an optimal binary threshold for balancing pseudo-changes and an unchanged area is difficult to obtain given the uncertainty and complexity of a changed area in terms of change magnitude and spatial distribution.…”

Section: Introductionmentioning

confidence: 99%

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Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images

Liu

Cheng

et al. 2019

IEEE Access

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Land cover change detection (LCCD) based on bitemporal remote sensing images has become a popular topic in the field of remote sensing. Despite numerous methods promoted in recent decades, an improvement on the usability and performance of these methods has remained necessary. In this paper, a novel LCCD approach based on the integration of k-means clustering and adaptive majority voting (k-means_AMV) techniques have been developed. The proposed k-means_AMV method consists of three major techniques. First, to utilize the contextual information in an adaptive manner, an adaptive region around a central pixel is constructed by detecting the spectral similarity between the central pixel and its eight neighboring pixels. Second, when the extension for the adaptive region is terminated, the k-means clustering method is applied to determine the label of each pixel within the adaptive region. Finally, an existing AMV technique is used to refine the label of the central pixel of the adaptive region. When change magnitude image (CMI) is scanned and processed in this manner, the label of each pixel in the CMI can be refined and the binary change detection map can be generated. Three image scenes related to different land cover change events are adapted to test the effectiveness and performance of the proposed k-means_AMV approach. The results show that the proposed k-means_AMV approach demonstrates better detection accuracies and visual performance than that of the several extensively used methods. INDEX TERMS Adaptive majority voting, k-means clustering, land cover change detection, remote sensing images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images

Liu

Cheng

et al. 2019

IEEE Access

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“…Although change detection based on region segmentation has some advantages, the global information of images is missed, and manual feature descriptors are required [23]. More approaches used for change detection are reported in References [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images

Lei

Xue

et al. 2018

Remote Sensing

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Change detection approaches based on image segmentation are often used for landslide mapping (LM) from very high-resolution (VHR) remote sensing images. However, these approaches usually have two limitations. One is that they are sensitive to thresholds used for image segmentation and require too many parameters. The other one is that the computational complexity of these approaches depends on the image size, and thus they require a long execution time for very high-resolution (VHR) remote sensing images. In this paper, an unsupervised change detection using fast fuzzy c-means clustering (CDFFCM) for LM is proposed. The proposed CDFFCM has two contributions. The first is that we employ a Gaussian pyramid-based fast fuzzy c-means (FCM) clustering algorithm to obtain candidate landslide regions that have a better visual effect due to the utilization of image spatial information. The second is that we use the difference of image structure information instead of grayscale difference to obtain more accurate landslide regions. Three comparative approaches, edge-based level-set (ELSE), region-based level-set (RLSE), and change detection-based Markov random field (CDMRF), and the proposed CDFFCM are evaluated in three true landslide cases in the Lantau area of Hong Kong. The experiments show that the proposed CDFFCM is superior to three comparative approaches in terms of higher accuracy, fewer parameters, and shorter execution time.

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“…For example, Celik et al proposed a method based on PCA and k-means clustering (PCA_Kmeans) through splitting the difference image into several h × h overlapping blocks where h is several pixels [43]; Lv et al presented a contextual analysis-based LCCD approach using a regular sliding window technique [44]. In recent years, level sets have been found to be helpful for describing the contour of objects and extract contextual information of remote sensing images for LCCD.…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Liu

Zhang

et al. 2018

Remote Sensing

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Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy.

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Contextual Analysis Based Approach for Detecting Change from High Resolution Satellite Imagery

Cited by 11 publications

References 15 publications

Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images

Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images

Unsupervised Change Detection Using Fast Fuzzy Clustering for Landslide Mapping from Very High-Resolution Images

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

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