A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification

Hung, Chih‐Cheng; Kulkarni, Shreyas; Kuo, Bor‐Chen

doi:10.1109/jstsp.2010.2096797

Cited by 106 publications

(39 citation statements)

References 18 publications

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“…Many feature extraction and dimension reduction techniques have been developed for hyperspectral image classification, such as principal component analysis (PCA) [29], independent component analysis [77], signal subspace identification [7], discrete wavelet transform [45], band reduction based on rough sets [66], projection pursuit algorithm [44], and clonal selection feature-selection algorithm [79]. The Fuzzy C-Means (FCM) algorithm is a well-known tool to find proper clusters, which can be used for hyperspectral image classification [43], and can be further enhanced by the Support Vector Domain Description [65].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Using Graph Clustering Methods

Zhang¹,

Merkurjev²,

Koniges³

et al. 2017

Image Processing on Line

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Hyperspectral imagery is a challenging modality due to the dimension of the pixels which can range from hundreds to over a thousand frequencies depending on the sensor. Most methods in the literature reduce the dimension of the data using a method such as principal component analysis, however this procedure can lose information. More recently methods have been developed to address classification of large datasets in high dimensions. This paper presents two classes of graph-based classification methods for hyperspectral imagery. Using the full dimensionality of the data, we consider a similarity graph based on pairwise comparisons of pixels. The graph is segmented using a pseudospectral algorithm for graph clustering that requires information about the eigenfunctions of the graph Laplacian but does not require computation of the full graph. We develop a parallel version of the Nyström extension method to randomly sample the graph to construct a low rank approximation of the graph Laplacian. With at most a few hundred eigenfunctions, we can implement the clustering method designed to solve a variational problem for a graph-cut-based semi-supervised or unsupervised classification problem. We implement OpenMP directive-based parallelism in our algorithms and show performance improvement and strong, almost ideal, scaling behavior. The method can handle very large datasets including a video sequence with over a million pixels, and the problem of segmenting a data set into a pre-determined number of classes. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.txt file of the archive.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Using Graph Clustering Methods

Zhang¹,

Merkurjev²,

Koniges³

et al. 2017

Image Processing on Line

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“…It aims at partitioning an image into disjoint meaningfully homogeneous regions. In the past decades, many different image segmentation techniques have been proposed [1,2,3,4]. Among them, those based on fuzzy c-means (FCM) [5] and Markov random field (MRF) models [6] are the most popular ones.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of high resolution remote sensing images by combining hidden Markov random field model and fuzzy c-means at the region level

Xu¹,

Li²

2016

Proceedings of the 2016 4th International Conference on Electrical &Amp; Electronics Engineering and Computer Science (ICEEECS

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Abstract. In high spatial resolution remote-sensing images, complex landscapes are usually accompanied with macro texture patterns, which often adversely affect segmentation accuracy, mainly due to their high spatial and spectral heterogeneity. To address this problem, this study develops an image segmentation method by combining the iteration procedure of fuzzy c-means (FCM) clustering and hidden Markov random field (HMRF) model at the region level. The performance of the proposed method was assessed through aerial images. Results indicate that the proposed method can improve image segmentation accuracy, compared to FLICM, HMRF-FCM, MRR-MRF, and IRGS.

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“…Image segmentation is widely used, almost all areas of image processing associated with image segmentation applications. In a variety of imaging applications, image segmentation is so inseparable for image target extraction, measure, such as: industrial automation, process control, document image processing, image coding, biomedical image analysis, as well as military, sports, agriculture engineering [1] . Among the image segmentation methods, the method based on clustering is one of the widely used.…”

Section: Introductionmentioning

confidence: 99%

Research of Image Segmentation Algorithm Based on Clustering

Wan¹,

Wang²

2016

Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications

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Abstract. Through the study of existing image segmentation algorithms, this paper improves the standard FCM-based image segmentation algorithm. And according to the principles of effective treatment on neighborhood image noise by mean filtering and median filtering, proposes a new similarity distance calculation method, which gives full consideration to the gray information and neighborhood information of pixels. Experimental results show that this improved algorithm has higher segmentation efficiency, while having a high segmentation accuracy and strong noise immunity.

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A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification

Cited by 106 publications

References 18 publications

Hyperspectral Image Classification Using Graph Clustering Methods

Hyperspectral Image Classification Using Graph Clustering Methods

Segmentation of high resolution remote sensing images by combining hidden Markov random field model and fuzzy c-means at the region level

Research of Image Segmentation Algorithm Based on Clustering

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