Rain-Contaminated Region Segmentation of X-Band Marine Radar Images With an Ensemble of SegNets

Chen, Xinwei; Huang, Weimin; Haller, Merrick C.; Pittman, Randall

doi:10.1109/jstars.2020.3043739

Cited by 17 publications

(7 citation statements)

References 43 publications

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“…To accurately acquire margin slices, locating the edge of the dark area is necessary. Conventional edge detection algorithms rely on the pixel's gradient around the edge to discriminate the operator's boundary [61][62][63]. However, for the SAR shadow edge region, the gradient is smooth and slowly changed, which is hard to detect the edge.…”

Section: A Analysis For the Dark Area In Airborne Sar Imagesmentioning

confidence: 99%

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Zhu¹

2021

IEEE Access

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The dark area in the SAR image corresponds to the zero-energy electromagnetic echo region because specular reflection occurs when the emitted electromagnetic wave (EMW) comes into contact with a smooth object on the ground. In addition, EWM sometimes cannot measure the entire portion of the ground object due to the incident angle of the actual aperture radar sensor and the height of the object on the ground, which creates a dark area in the SAR image called a SAR shadow. Therefore, it is challenging to distinguish the dark area from shadows or some ground objects with smooth surfaces. To address this problem, this paper proposes a novel deep marginal learning algorithm (DMLA) to implement the dark zone classification of airborne SAR images. Specifically, a novel Snake Search Algorithm (SSA) with a chain code is deployed to generate margin slices of dark areas in SAR images, which is used as the input to the following classification network. In addition, a deep convolutional neural network with simple architecture is constructed to train the margin slices of the SSA, which outputs the final classification results with labels. Finally, experiments on three public airborne SAR image datasets output the average classification accuracy of 98.775% for the dark margin slices and an accuracy of 100% to identify dark regions in the original SAR images. The proposed method not only contributes to a high classification accuracy but also improves time efficiency.

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Section: A Analysis For the Dark Area In Airborne Sar Imagesmentioning

confidence: 99%

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Zhu¹

2021

IEEE Access

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“…Land-based and ship-borne low-cost X-band marine radars with high temporal and spatial resolution have been broadly used in the observation of ocean wave height [8], currents [9], rain [10], and sea surface winds [11]. The average wind field information retrieved from X-band marine radar is one of the more important environmental factors for continuous and real-time takeoff and landing safety of carrier-borne aircraft under the conditions of sea dynamic vehicles.…”

Section: Introductionmentioning

confidence: 99%

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

Wang

Qiu

et al. 2023

Remote Sensing

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The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.

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“…Assuming there are sufficiently high waves in the view field of the radar, their visibility in marine X-band radar images and hence the quality and reliability of the wave observation depends on environmental conditions, e.g., wind, which is responsible for creating sea clutter. Also rain clutter or interference in the image can camouflage the actual wave patterns and hence can degrade the quality of the sea state estimations ([ 20 , 21 , 22 , 23 ]).…”

Section: Introductionmentioning

confidence: 99%

On the Effect of Interferences on X-Band Radar Wave Measurements

Chernyshov

Hessner

Zavadsky

et al. 2022

Sensors

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X-band radars are in growing use for various oceanographic purposes, providing spatial real-time information about sea state parameters, surface elevations, currents, and bathymetry. Therefore, it is very appealing to use such systems as operational aids to harbour management. In an installation of such a remote sensing system in Haifa Port, consistent radially aligned spikes of brightness randomly distributed with respect to azimuth were identified. These streak noise patterns were found to be interfering with the common approach of oceanographic analysis. Harbour areas are regularly frequented with additional electromagnetic transmissions from other ship and land-based radars, which may serve as a source of such interference. A new approach is proposed for the filtering of such undesirable interference patterns from the X-band radar images. It was verified with comparison to in-situ measurements of a nearby wave buoy. Regardless of the actual source of the corresponding pseudo-wave energy, it was found to be crucial to apply such filtration in order to improve the performance of the standard oceanographic parameter retrieval algorithm. This results in better estimation of the mean sea state parameters towards lower values of the significant wave height. For the commercial WaMoSII system this enhancement was clearly apparent in the improvement of the built-in quality control criteria marks. The developed prepossessing procedure improves the robustness of the directional spectra estimation practically eliminating pseudo-wave energy components. It also extends the system’s capability to measure storm events earlier on, a fact that is of high importance for harbour operational decision making.

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Rain-Contaminated Region Segmentation of X-Band Marine Radar Images With an Ensemble of SegNets

Cited by 17 publications

References 43 publications

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Dark Area Classification From Airborne SAR Images Based on Deep Margin Learning

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

On the Effect of Interferences on X-Band Radar Wave Measurements

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