Efficient texture analysis of SAR imagery

Kandaswamy, Umasankar; Adjeroh, Don; Lee, M.C.

doi:10.1109/tgrs.2005.852768

Cited by 106 publications

(37 citation statements)

References 23 publications

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“…Thus, it is generally accepted that the use of textural images improves the accuracy of land cover classification [22][23][24][25]. Previous research on texture feature extraction showed that the gray-level co-occurrence matrix (GLCM) is one of the most trustworthy methods for classification [26,27].…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

Zakeri¹,

Yamazaki²,

Li³

2017

Applied Sciences

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Abstract:Land cover classification of built-up and bare land areas in arid or semi-arid regions from multi-spectral optical images is not simple, due to the similarity of the spectral characteristics of the ground and building materials. However, synthetic aperture radar (SAR) images could overcome this issue because of the backscattering dependency on the material and the geometry of different surface objects. Therefore, in this paper, dual-polarized data from ALOS-2 PALSAR-2 (HH, HV) and Sentinel-1 C-SAR (VV, VH) were used to classify the land cover of Tehran city, Iran, which has grown rapidly in recent years. In addition, texture analysis was adopted to improve the land cover classification accuracy. In total, eight texture measures were calculated from SAR data. Then, principal component analysis was applied, and the first three components were selected for combination with the backscattering polarized images. Additionally, two supervised classification algorithms, support vector machine and maximum likelihood, were used to detect bare land, vegetation, and three different built-up classes. The results indicate that land cover classification obtained from backscatter values has better performance than that obtained from optical images. Furthermore, the layer stacking of texture features and backscatter values significantly increases the overall accuracy.

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Section: Introductionmentioning

confidence: 99%

Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

Zakeri¹,

Yamazaki²,

Li³

2017

Applied Sciences

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“…The most straightforward approach is to regard the scattering coefficient or the coherence/incoherence matrix as the underlying image features. Besides polarimetric features, texture has been proven as an efficient feature for image classification [10], such as the gray-level co-occurrence matrix [11], the wavelet with statistic textures [12], discrete wavelet transform [13], the semi-variance graph [14], etc. In 2011, Dai [15] put forward a multi-level local histogram descriptor, which is robust to speckle noise.…”

Section: Sar Images Classificationmentioning

confidence: 99%

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Fan

Feng

et al. 2017

Remote Sensing

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This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification.(1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents' regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy.

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“…In addition, the objects in SAR images are also spatially related. Thus, many kinds of features are extracted for SAR images, such as the brightness after denoising, 3,4 texture, [5][6][7][8][9] and edges. [10][11][12][13] These features can describe some properties of SAR images, but no single feature can completely and accurately characterize miscellaneous objects in SAR images.…”

Section: Introductionmentioning

confidence: 99%

“…One direct solution to improve the accuracy and robustness of the objective function is to extract more information from SAR images. Such considerations have driven the emergence of a large amount of literatures [5][6][7][8][9]20,27,29,36,37 concerning the texture classification of SAR images. Clausi 9 carefully compared and integrated different texture features into the classification task of SAR ice images.…”

Section: Introductionmentioning

confidence: 99%

“…Clausi 9 carefully compared and integrated different texture features into the classification task of SAR ice images. Kandaswamy et al 8 proposed a statistical occupancy model to analyze the efficiency of different texture features in SAR image classification. However, so far as we know, little research has been done to combine texture and brightness together to describe SAR images, which are two different and complementary features for SAR image interpretation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Efficient texture analysis of SAR imagery

Cited by 106 publications

References 23 publications

Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery

Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

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

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