Unsupervised Change Detection of Remotely Sensed Images Using Fuzzy Clustering

Ghosh, Susmita; Mishra, Niladri Shekhar; Ghosh, Ashish

doi:10.1109/icapr.2009.82

Cited by 13 publications

(9 citation statements)

References 14 publications

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“…For the cluster of each pixel in the feature space, existing uncertainty of the number of categories and the massive overlap of feature spaces of different categories, the pixels belonging to different categories cannot be absolutely separable by sharp boundaries. Therefore, a fuzzy clustering technique [ 9 , 34 ] is more appropriate to separate overlapping clusters. In our problem, we assume that a pixel can belong to multiple different categories with certain degrees of membership due to no clear boundary between them.…”

Section: Methodsmentioning

confidence: 99%

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

Hou

Wang

Liu

2016

Sensors

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Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

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Section: Methodsmentioning

confidence: 99%

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

Hou

Wang

Liu

2016

Sensors

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“…We can do this by controlling the decay function η(t) used in (10). Here, we set η 0 in (11) to be proportional with N as follows:…”

Section: E Effects Of Data Set Cardinality On αmentioning

confidence: 99%

“…Celik [9] employed c-means clustering and principal component analysis to perform change detection on multitemporal satellite imagery. Gosh et al [10] found that change detection of multitemporal satellite imagery using fuzzy c-means (FCM) and Gustafson-Kessel clustering algorithms produced better results than those obtained using Markov random field and other neural-network-based algorithms. Carlotto [11] proposed cluster-based anomaly detection based on the Gaussian mixture model trained using the EM algorithm to detect man-made objects in multitemporal multiband imagery where change pixels were found by detecting significant deviations from the distribution of a cluster containing mostly background pixels.…”

Section: Introductionmentioning

confidence: 97%

Clustering of Detected Changes in High-Resolution Satellite Imagery Using a Stabilized Competitive Agglomeration Algorithm

Sjahputera

Scott

Claywell

et al. 2011

IEEE Trans. Geosci. Remote Sensing

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The Geospatial Change Detection and eXploitation (GeoCDX) is a fully automated system for detection and exploitation of change between multitemporal high-resolution satellite and airborne images. Overlapping multitemporal images are first organized into 256 m × 256 m tiles in a global grid reference system. The system quantifies the overall amount of change in a given tile with a tile change score as an aggregation of pixel-level changes. The tiles are initially ranked by these change scores for retrieval, review, and exploitation in a Web-based application. However, the ranking does not account for the wide variety of change types that are typically observed in the top-ranked change tiles. To automatically organize the wide variety of change patterns observed in multitemporal high-resolution imagery, we perform tile clustering using the competitive agglomeration (CA) algorithm stabilized using the fuzzy c-means (FCM) algorithm. Each resulting cluster contains tiles with a visually similar type of change. By visual inspection of these tile clusters, GeoCDX users can quickly find certain types of change without having to sift through a large number of tiles initially organized solely by their tile change score, thereby reducing the time it takes for users to discover and exploit the change pattern(s) of greatest interest to a given application (e.g., urban growth, disaster assessment, facility monitoring, etc.). The tile clusters also provide a high-level overview of the various types of change that occur between the two observations. This overview is compared with a similar yet more limited view offered by a relevance feedback tool that requires a user to select sample tiles for use as samples in the reranking process.

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“…Image change detection refers to the detection of landform changes in remote sensing images obtained at different times in the same area. Recently, with the development of remote sensing technology, optical remote sensing image change detection has been widely used in the fields of environmental monitoring, crop measurement, urban research, ecosystem monitoring, natural disaster assessment, battlefield target strike effect evaluation and military reconnaissance [1,2,3,4,5]. There are many algorithms for remote sensing image change detection [6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm

Yang

Jia

Yang

et al. 2019

Sensors

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The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. Based on the change in the wavelet coefficient, the high- and low-frequency parts of the remote sensing image are replaced separately, and the low-frequency clouds are suppressed while maintaining the high-frequency detail of the image, which achieves good results. Then, an unsupervised change detection algorithm based on a combined difference graph and fuzzy c-means clustering algorithm (FCM) clustering is applied. First, the image is transformed into a logarithmic domain, and the image is denoised using Frost filtering. Then, the mean ratio method and the difference method are used to obtain two graph difference maps, and the combined difference graph method is used to obtain the final difference image. The experimental results show that the algorithm can effectively solve the problem of image change detection under thin cloud interference.

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Unsupervised Change Detection of Remotely Sensed Images Using Fuzzy Clustering

Cited by 13 publications

References 14 publications

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

Clustering of Detected Changes in High-Resolution Satellite Imagery Using a Stabilized Competitive Agglomeration Algorithm

Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm

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