Remote Sensing Classification Using Fuzzy C-means Clustering with Spatial Constraints Based on Markov Random Field

Yang, Honglei; Peng, Junhuan; Xia, Bairu; Zhang, Dingxuan

doi:10.5721/eujrs20134617

Cited by 29 publications

(16 citation statements)

References 17 publications

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“…Classes were defined through a set of rules and organized in hierarchical groups, so that a child class inherited properties from the parent one. The classification process was carried out either by specifying thresholds for each rule (crisp classification) [Comber et al, 2012], by specifying a set of probability density functions (fuzzy classification) [HongLei et al, 2013] or through a k-Nearest Neighbour (NN) approach [Chirici et al, 2012]. Figure 5 shows the workflow.…”

Section: Image Classificationmentioning

confidence: 99%

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Gianinetto

Rusmini²,

Candiani

et al. 2014

European Journal of Remote Sensing

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Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen's kappa coefficient of 0.84 and an overall accuracy of 85%.

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Section: Image Classificationmentioning

confidence: 99%

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Gianinetto

Rusmini²,

Candiani

et al. 2014

European Journal of Remote Sensing

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“…In traditional methods using high-resolution image data, the "salt and pepper" effect is very obvious and the rich textural information will be incorrectly considered as noise. This has a great impact on the extracted change information [Blaschke, 2010;Yang et al, 2013;. Therefore, research into change detection methods based on high-resolution images is valuable [Klemas, 2013].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Change Information based on Multi-sequence Image Objects

Zhang

Cheng

2016

European Journal of Remote Sensing

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For identification of change information, most studies focus on pixel-based techniques for low-resolution images, while few studies have examined object-based techniques for high-resolution images. Moreover, most of the techniques are complex and have a high requirement for the segmentation scale. This paper proposes a change detection method based on multi-sequence image objects and introduces the use of arithmetic progression to generate the set of segmentation scales. Pre-event and post-event images are segmented with multi-scales, respectively, and sub-objects are obtained based on the division of the minimum segmentation scales of bi-temporal images. Change feature vectors are constructed for each associated object of sub-object and vectors' magnitude is computed. After the determination of change threshold values, the change feature vectors are used to confirm whether sub-objects have changed, providing final change information. This method was tested using the bi-temporal World View 2 images taken before and after a landslide. The results confirm the feasibility of the method presented in this paper, and show its high accuracy through a comparison with the changing vector analysis method and the post-classification comparison method based on object-oriented theory. The approach outlined herein would be helpful for extraction of change information in high-resolution images.

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“…Using texture information [16], [17], applying simple majority filter [4], segmentation-based clustering [15], [18], using Markov random fields (MRFs) [19], [20], using gradient and edge information [11], [16], spatial prior probability determination [10], and local embedding [21] are some methods for incorporating image spatial information into the clustering process. Spatial features such as texture and applying majority filter on the clustering results can incorporate some spatial aspects of data into the feature space, but they do not have a comprehensive strategy for image clustering [12].…”

Section: Introductionmentioning

confidence: 99%

Clustering Multispectral Images Using Spatial–Spectral Information

Fatemi

Mobasheri

Abkar

2015

IEEE Geosci. Remote Sensing Lett.

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Clustering is an important topic in image analysis and has many applications. Owing to the limitations of the feature space in multispectral images and spectral overlap of the clusters, it is required to use some additional information such as the spatial context in image clustering. To increase the accuracy of image clustering, a new Hierarchical Iterative Clustering Algorithm using Spatial and Spectral information (HICLASS) is introduced. This algorithm separates pixels into uncertain and certain categories based on decision distances in the feature space. The algorithm labels the certain pixels using the k-means clustering, and the uncertain ones with the help of information in both spatial and spectral domains of the image. The proposed algorithm is tested using simulated and real data. The benchmark results indicate better performance of HICLASS when compared with the k-means, local embeddings, and some proximity-based algorithms. The overall accuracy of the k-means has increased between 12.5% and 20.4% for different data. The HICLASS method increases the accuracy and generates more homogeneous regions, which are required for object-based applications.

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Remote Sensing Classification Using Fuzzy C-means Clustering with Spatial Constraints Based on Markov Random Field

Cited by 29 publications

References 17 publications

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Extraction of Change Information based on Multi-sequence Image Objects

Clustering Multispectral Images Using Spatial–Spectral Information

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