A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images

Akbarizadeh, Gholamreza

doi:10.1109/tgrs.2012.2194787

Cited by 268 publications

(97 citation statements)

References 22 publications

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“…To validate the performance of the proposed method, we use both types of images in quantitative evaluation and visualization results. We mainly compare our results with the results of previous studies [3,5,16,17], in which their parameters are tuned to obtain the best results. Figures 8a, 9a and 10a are the synthetic SAR images, which are from the Brodatz database.…”

Section: Resultsmentioning

confidence: 86%

“…In our method, H = 6 and ∆T is 0.221. The scale (patch) size in the support vector machine (SVM) [3], SRC [5] and JSRM [16] is fixed and we set it to be 3 × 3. The ground truth was used to calculate the accuracy of the classification results to evaluate the contrast algorithms.…”

Section: Results On Synthetic Sar Imagesmentioning

confidence: 99%

“…In recent years, SAR image classification has received more attention as an important part of image understanding and interpretation. A considerable number of image classification algorithms have been proposed, such as support vector machine (SVM) [3], neural network (NN) [4], wavelet decomposition, and sparse representation classification (SRC) [5], etc. Among these existing methods, the traditional SVM and NN methods show high reliability in pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images

Zhai

Wen

et al. 2017

Remote Sensing

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Abstract:A multi-layer classification approach based on multi-scales and multi-features (ML-MFM) for synthetic aperture radar (SAR) images is proposed in this paper. Firstly, the SAR image is partitioned into superpixels, which are local, coherent regions that preserve most of the characteristics necessary for extracting image information. Following this, a new sparse representation-based classification is used to express sparse multiple features of the superpixels. Moreover, a multi-scale fusion strategy is introduced into ML-MFM to construct the dictionary, which allows complementation between sample information. Finally, the multi-layer operation is used to refine the classification results of superpixels by adding a threshold decision condition to sparse representation classification (SRC) in an iterative way. Compared with traditional SRC and other existing methods, the experimental results of both synthetic and real SAR images have shown that the proposed method not only shows good performance in quantitative evaluation, but can also obtain satisfactory and cogent visualization of classification results.

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Section: Resultsmentioning

confidence: 86%

Section: Results On Synthetic Sar Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images

Zhai

Wen

et al. 2017

Remote Sensing

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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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“…Since the proposed speed function tends to zero when (T(ψ(x)) − T(I t )) 2 tends to ε [11], which is a small value, the evolving contour will stop at a certain pixel whose intensity and texture information are equal to those of the selected region. Now, we can figure out the desired multi-kernel level set equation according to Equations (5)- (10):…”

Section: Level Set Equationmentioning

confidence: 99%

Embedded Implementation of VHR Satellite Image Segmentation

2017

Architecture‐Aware Optimization Strategies in Real‐time Image Processing

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Processing and analysis of Very High Resolution (VHR) satellite images provide a mass of crucial information, which can be used for urban planning, security issues or environmental monitoring. However, they are computationally expensive and, thus, time consuming, while some of the applications, such as natural disaster monitoring and prevention, require high efficiency performance. Fortunately, parallel computing techniques and embedded systems have made great progress in recent years, and a series of massively parallel image processing devices, such as digital signal processors or Field Programmable Gate Arrays (FPGAs), have been made available to engineers at a very convenient price and demonstrate significant advantages in terms of running-cost, embeddability, power consumption flexibility, etc. In this work, we designed a texture region segmentation method for very high resolution satellite images by using the level set algorithm and the multi-kernel theory in a high-abstraction C environment and realize its register-transfer level implementation with the help of a new proposed high-level synthesis-based design flow. The evaluation experiments demonstrate that the proposed design can produce high quality image segmentation with a significant running-cost advantage.

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A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images

Cited by 268 publications

References 22 publications

Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images

Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images

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

Embedded Implementation of VHR Satellite Image Segmentation

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