Speckle-Free SAR Image Ship Detection

Chen, Siwei; Cui, Xing-Chao; Wang, Xuesong; Xiao, Shunping

doi:10.1109/tip.2021.3089936

Cited by 52 publications

(26 citation statements)

References 43 publications

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“…Note that the NL-SAR parameter values correspond to the filter window sizes applied in the remaining filtering methods. We also used nonfiltered data in form of a coherency matrix, since some SAR methods are based on speckled data [32].…”

Section: A Data Acquisition and Processingmentioning

confidence: 99%

On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes

Gierszewska

Berezowski

2022

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

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Previous wetlands studies have thoroughly verified the usefulness of data from synthetic aperture radar (SAR) sensors in various acquisition modes. However, the effect of the processing parameters in wetland classification remains poorly explored. In this study, we investigated the influence of speckle filters and decomposition methods with different combinations of filter and decomposition windows sizes on classification accuracy. We used a C-band Radarsat 2 image acquired over a wetland located in northeast Poland. We processed the SAR data using various speckle filters: boxcar, intensity-driven adaptive-neighborhood (IDAN), improved Lee sigma, refined Lee (in 5×5 to 11×11 pixel window sizes), and a nonlocal NL-SAR. Next, we processed the nonfiltered and filtered data using nine polarimetric decompositions, also in 5×5 to 11×11 pixel window sizes. The extracted polarimetric features were applied as an input dataset in the random forest classification model in single-and multidecomposition scenarios. In the single-decomposition scenario, the Cloude-Pottier decomposition produced the highest (72%) and the Touzi decomposition achieved the lowest (38%) accuracy. The IDAN filter with an 11×11 filter window and a 9×9 decomposition window had the highest, and the nonfiltered data with a 5×5 decomposition window had the lowest accuracy in the multidecomposition scenario. The most important features were the alpha parameter from the Cloude-Pottier decomposition, the polarimetric contribution of the Shannon entropy, and the volume backscattering components. The results stress the importance of appropriate processing parameters in the SAR data classification workflow, and the study guides in selecting the most suitable combination of radar image processing parameters for wetland classification.

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Section: A Data Acquisition and Processingmentioning

confidence: 99%

On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes

Gierszewska

Berezowski

2022

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

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“…There are many granular specks distributed on the image, which is called speckle noise. Speckle is commonly interpreted as a kind of locally correlated noise that reduces image contrast and conceals fine feature details, causing negative effects on target detection and recognition [2,3] scene segmentation [4], and image registration [5]. In consideration of the damaging effect of speckle on images, speckle suppression is required to smooth uniform areas of the images and preserve the features, like edges and textures.…”

Section: Introductionmentioning

confidence: 99%

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Wang

et al. 2022

Remote Sensing

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Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising models is proposed in this paper. Taking full account of the local statistics in the image, a quantization technique—which is different from the normal edge detection method—is supported by the variation coefficient of the image. Accordingly, a quantizer is designed to respond to both noise level and edge strength. This quantizer automatically determines the threshold of diffusion coefficient and controls the weight between total variation filter and anisotropic diffusion partial differential equation filter. A series of experiments are conducted to test the performance of the quantizer and proposed filter. Extensive experimental results have demonstrated the superiority of the proposed method with both synthetic images and natural SAR images.

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“…In addition, ship detection methods based on ship features are widely utilized. Common ship features include geometric features [9], [10], [11], [12], orientation features [13], structural features [14], [15], polarization features [16], [17], [18], [19], [20], and scattering features [21], [22], [23]. Recently, Zhu et al [24] proposed a projection shape template-based ship recognition method.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Improvement of Generalization Performance of SAR Ship Recognition Algorithms

Zhang

et al. 2022

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

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As artificial intelligence continues to advance, deep learning has greatly contributed to the advancement of ship recognition using Synthetic Aperture Radar (SAR) images. Deep learning-based SAR ship recognition performance is largely dependent on the sample set used. SAR ship recognition datasets published in recent years, however, are most derived from a single SAR satellite sensor. It needs to be evaluated and analyzed carefully whether the model trained by a single satellite dataset can still achieve the same accuracy with different SAR satellites. The paper focuses on the following research to address these issues. Firstly, using multiple SAR satellite sensors, we create a new SAR ship dataset (named generalization performance evaluation dataset, GPED) containing multi-resolution and multipolarization data to examine the generalization performance of the deep learning-based SAR ship recognition method. GPED and a marine target detection dataset (MTDD) are then used to evaluate and analyze the generalization performance of current mainstream deep learning methods. According to the the experiment results, the mean average accuracy (mAP) of the ship recognition model trained on GPED is generally higher than that of MTDD, which proves that GPED has a better generalization performance. Furthermore, SAR ship detection datasets have more samples than ship recognition datasets, which inspired us to use transfer learning to transfer knowledge from ship detection to ship recognition. In this paper, a method for ship recognition based on transfer learning that utilizes the knowledge gained from the ship detection task is proposed. The method includes two modules: pretraining module and fine-tuning module. It can apply samples of unlabeled ship types to ship recognition, thus reducing the number of labeled samples that are required for ship recognition. The experimental results on GPED and MTDD show that our method can achieve good recognition performances.

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Speckle-Free SAR Image Ship Detection

Cited by 52 publications

References 43 publications

On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes

On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Evaluation and Improvement of Generalization Performance of SAR Ship Recognition Algorithms

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