Context-Based Hierarchical Unequal Merging for SAR Image Segmentation

Yu, Hui; Zhang, Xiangrong; Wang, Shuang; Hou, Biao

doi:10.1109/tgrs.2012.2203604

Cited by 58 publications

(57 citation statements)

References 29 publications

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“…4. Context-based hierarchical unequally merging for SAR image segmentation (CHUMSIS), 35 which models the context of oversegmented regions by three rules inspired from the Gestalt laws and then designs two different strategies to combine the rules for the region merging. The rules in CHUMSIS are different in essence from the fuzzy operators adopted in this paper.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, it is quite necessary to integrate the brightness and the texture together to characterize SAR images. However, most of the existing segmentation methods for SAR images rely on either brightness or texture, and only a few recent 35,47 incorporate brightness and texture simultaneously into the same framework. One major reason is the different structures (such as dimensions and ranges) of the brightness feature and the texture feature.…”

Section: Multifeatures Extraction For Synthetic Aperture Radar Imagesmentioning

confidence: 99%

“…However, due to speckle noise and the coexistence of multiscale objects, it is not as easy to accurately extract edges in SAR images as in ordinary images. In this paper, a multiscale edge detector 35 is utilized to extract the edge features, as shown below. The input image is first convolved with a bank of Prewitt kernels of different scales to obtain the multiscale edge images, which can be written as below.…”

Section: Multifeatures Extraction For Synthetic Aperture Radar Imagesmentioning

confidence: 99%

“…Researchers have proposed many kinds of segmentation algorithms for SAR images, which include threshold methods, 3,[17][18][19] spectral clustering (SC) algorithms, 20,21 statistic model-based methods, 1,14,15,[22][23][24][25] artificial intelligence methods, [26][27][28][29][30] support vector machine (SVM), 6,31 region growing methods, 15,[32][33][34][35] and so on. Among these algorithms, cluster-based algorithms form one popular and representative family, whose main idea is to group pixels in such a way that the pixels in the same group are more similar to each other than those in other groups.…”

Section: Introductionmentioning

confidence: 99%

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Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

Feng

2015

J. Appl. Remote Sens

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Abstract. Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Methodsmentioning

confidence: 99%

Section: Multifeatures Extraction For Synthetic Aperture Radar Imagesmentioning

confidence: 99%

Section: Multifeatures Extraction For Synthetic Aperture Radar Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

Feng

2015

J. Appl. Remote Sens

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“…Superpixel-based classifications were also performed over aerial images [18] and Polarimetric SAR (PolSAR) imagery [24]. In [25], superpixels are used as operation units in order to segment PolSAR images in a hierarchical manner. Segmentation at each level is made through a merging process, in which adjacent superpixels are joined according to similar criteria based on contextual information.…”

Section: Introductionmentioning

confidence: 99%

A GEOBIA Methodology for Fragmented Agricultural Landscapes

et al. 2015

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Abstract:Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach.

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Certain investigations on image segmentation algorithms on synthetic aperture radar images and classification using convolution neural network

Srinitya¹,

Sharmila

Logeswari³

et al. 2021

Concurrency and Computation

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Synthetic aperture radar (SAR) image segmentation is the most important step in detecting the land and sea areas in many applications like military in particular. This article experiments four prominent image segmentation algorithms on SAR images and we propose a hybrid method to segment SAR images, which shows better efficiency.First among them is OTSU thresholding method that segmented the image with less interest in the texture component. Further, adaptive thresholding, artificial bee colony segmentation, and wavelet transforms were experimented; wavelet transforms give better segmentation results compared to thresholding methods. In this article, wavelet along with masking threshold including segmentation method is proposed, which gives a better result based on texture and executes in almost the same time as other methods and the segmented images give better classification results using convolution neural network when compared to other methods.

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Context-Based Hierarchical Unequal Merging for SAR Image Segmentation

Cited by 58 publications

References 29 publications

Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

Independent feature subspace iterative optimization based fuzzy clustering for synthetic aperture radar image segmentation

A GEOBIA Methodology for Fragmented Agricultural Landscapes

Certain investigations on image segmentation algorithms on synthetic aperture radar images and classification using convolution neural network

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