Sparse Hierarchical Clustering for VHR Image Change Detection

Ding, Kun; Huo, Chunlei; Xu, Yuan; Zhi, Zhao; Chen, Pan

doi:10.1109/lgrs.2014.2351807

Cited by 36 publications

(15 citation statements)

References 11 publications

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“…Seven widely-used methods are chosen to be compared, including the EM-based method [ 5 ], the MRF-based method [ 5 ], the PCA-based method [ 7 ], the parcel-based method [ 15 ], the MBI-based method [ 19 ], the sparse hierarchical clustering (SHC)-based method [ 21 ] and the fast object-level-based method [ 16 ]. Evaluation indexes: Five indexes are used to evaluate the accuracy of above-mentioned methods.…”

Section: Resultsmentioning

confidence: 99%

“…They combined several pieces of building information, including morphological building index (MBI), spectral and shape conditions for multitemporal high-resolution images. Ding et al [ 21 ] proposed a sparse hierarchical clustering approach for VHR image CD. They stacked bi-temporal multiscale center-symmetric local binary pattern features and learned a tree-structured dictionary.…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Ghosh et al [9] employ two fuzzy clustering algorithms for binary change detection in Landsat difference images. A technique from a different clustering domainsparse hierarchical clustering -is used by [10] for change detection in VHR imagery. It has to be noted that all of the mentioned studies either work directly on pixel-level or incorporate the spatial domain on a local neighborhood level (i.e.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of clustering algorithms for unsupervised change detection in VHR remote sensing imagery

Leichtle

Geiß

Wurm

et al. 2017

2017 Joint Urban Remote Sensing Event (JURSE)

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Remote sensing has proven to be an adequate tool for observation of changes to the Earth's surface. Especially modern space-borne sensors with very-high spatial resolution offer new capabilities for monitoring of dynamic urban environments. In this context, clustering is a well suited technique for unsupervised and thus highly automatic detection of changes. In this study, seven partitioning clustering algorithms from different methodological categories are evaluated regarding their suitability for unsupervised change detection. In addition, object-based feature sets of different characteristics are included in the analysis assessing their discriminative power for classification of changed against unchanged buildings. In general, the most important property of favorable algorithms is that they do not require additional arbitrary input parameters except the number of clusters. Best results were achieved based on the clustering algorithms k-means, partitioning around medoids, genetic k-means and self-organizing map clustering with accuracies in terms of κ statistics of 0.8 to 0.9 and beyond.

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“…Disaster management, urban studies, geology, mineral exploration, geo botany, planetary mapping, etc., are some of the applications of change detections [2].Change detection significantly reduces the ideal overlap present in previous reviews giving a compact nomenclature with which to understand and apply change detection work flows. The main idea of this paper is to highlight the changes PCA is a widely used image transform algorithm and it is a statistical procedure that uses an orthogonal conversion to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables [6].…”

Section: Introductionmentioning

confidence: 99%

“…Sparse hierarchical clustering method is used to cluster the change features such as center symmetric local binary pattern and to learn the tree structured dictionary from all the change features to represent the multimodal distribution [2]. It is used to overcome the limitations that are imposed on traditional clustering methods for unsupervised change detection of high resolution images due to multimodal distribution of change features.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Change Detection of Multispectral Imagery Using Multi Level Fuzzy Based Deep Representation

Usha

Vasuki

2017

Journal of Asian Scientific Research

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Article HistoryChange detection in remote sensing images provides useful information for various applications. This paper proposes a robust methodology for the analysis of multispectral imagery using Deep belief network (DBN) and Fuzzy interference system (FIS). Initially Euclidean distance and cosine angle distance features are extracted from the image. Deep learning is a robust machine learning method in which the extracted features are processed through set linear mapping and the changes are detected. However, the coarse spatial resolution indicating the intensity of modifications in class proportion instead of accounting for the change using discrete land covers classes is used in fuzzy image classification. Hence, the FIS is combined with DBN which allows defining our own rules to detect the changes accurately. It uses triangular membership function to plot the changes. The experimental results show that the proposed method enhanced the change detection by improving the performance parameters.

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Sparse Hierarchical Clustering for VHR Image Change Detection

Cited by 36 publications

References 11 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

Evaluation of clustering algorithms for unsupervised change detection in VHR remote sensing imagery

Unsupervised Change Detection of Multispectral Imagery Using Multi Level Fuzzy Based Deep Representation

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