PolSAR image segmentation based on feature extraction and data compression using Weighted Neighborhood Filter Bank and Hidden Markov random field-expectation maximization

Tirandaz, Zeinab; Akbarizadeh, Gholamreza; Kaabi, Hooman

doi:10.1016/j.measurement.2019.107432

Cited by 97 publications

(35 citation statements)

References 32 publications

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“…For quantitative performance comparison, the proposed method is benchmarked with the other three methods, including the abovementioned two superpixel-based segmentation methods and another segmentation method. Labelled as (1) in Table II, The third approach is based on the hidden Markov random field (HMRF) model and its expectation-maximization [29], which has been used for sea ice segmentation for its high accuracy and robustness in image segmentation [30]. Table II show the evaluation metrics computed from proposed method and other state-of-the-art methods.…”

Section: Quantitative Comparisonmentioning

confidence: 99%

Texture-Sensitive Superpixeling and Adaptive Thresholding for Effective Segmentation of Sea Ice Floes in High-Resolution Optical Images

Chai

Ren

Hwang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Efficient and accurate segmentation of sea ice floes from high-resolution optical (HRO) remote sensing images is crucial for understanding of sea ice evolutions and climate changes, especially in coping with the large data volume. Existing methods suffer from noise interference and mixture of water and ice caused high segmentation error and less robustness. In this study, we propose a novel sea ice floe segmentation algorithm from HRO images based on texture-sensitive superpixeling and twostage thresholding. First, sparse components are extracted from the HRO images using the Robust Principal Component Analysis (RPCA), and noise is removed by the bilateral filter. The enhanced image is obtained by combining the low-rank matrix and the sparse components. Second, a texture-sensitive Simple Linear Iterative Clustering (SLIC) superpixel algorithm is introduced for pre-segmentation of the enhanced HRO image. Third, a learning based adaptive thresholding in the two-stages is employed to generate the refined segmentation from the derived superpixels blocks. The efficacy of the proposed method is validated on two HRO images using visual assessment, quantitative evaluation (with seven metrics) and histogram comparison. The superior performance of the proposed method has demonstrated its efficacy for sea ice floe segmentation.

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Section: Quantitative Comparisonmentioning

confidence: 99%

Texture-Sensitive Superpixeling and Adaptive Thresholding for Effective Segmentation of Sea Ice Floes in High-Resolution Optical Images

Chai

Ren

Hwang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…N OWADAYS, deep convolutional neural networks have gained much attention in the application of synthetic aperture radar (SAR) field, such as automatic target recognition [1], urban interpretation [2], marine surveillance [3] and so on [4]. Among them, ship detection in SAR images has been widely studied due to its indispensable role in military and civil fields.…”

Section: Introductionmentioning

confidence: 99%

Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation

Chen

Zhan

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Even the statistical specificity of the SAR image is considered, there are still many false keypoints detected by literature [18]. In [21], a weighted neighborhood filter bank is proposed, which can extract spatial information and achieves a high discriminative power.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Zhang

Liu

Jiao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Patch-level labels are easy to achieve, and they require less expertise and lower resource consumption than pixellevel ones. Each patch has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and selection of discriminative features in PTM have a large impact on the classification results. Most of the existing feature learning methods do not make full use of high-level structure feature and the feature correlation within similar images to mine discriminative features. Therefore, this paper proposes a discriminative sketch topic model with structural constraint (C-SSTM) for SAR image classification. In the proposed model, each image patch is characterized by structural and texture features. In particular, the sketch structural feature is based on the sketch map to represent the image local structure pattern. Then the local image manifold information is preserved in terms of structure and texture. In the structural constraint, the texture and structure of each image patch are combined to learn discriminative latent semantic topics between image patches. The structural constraint enforces that the semantically similar features usually co-occur in similar images with a high probability. Finally, each image patch is quantified by discriminative latent semantic topics instead of lowlevel representation. The experimental results tested on synthetic and real SAR images demonstrate that the proposed C-SSTM is able to learn effective structural feature representation from SAR images. Compared with other related approaches, C-SSTM produces competitive classification accuracies with high time efficiency.

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PolSAR image segmentation based on feature extraction and data compression using Weighted Neighborhood Filter Bank and Hidden Markov random field-expectation maximization

Cited by 97 publications

References 32 publications

Texture-Sensitive Superpixeling and Adaptive Thresholding for Effective Segmentation of Sea Ice Floes in High-Resolution Optical Images

Texture-Sensitive Superpixeling and Adaptive Thresholding for Effective Segmentation of Sea Ice Floes in High-Resolution Optical Images

Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation

Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

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