An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

Liu, Chun; Yang, Jian; Zheng, Jiangbin; Nie, Xuan

doi:10.3390/rs14010205

Cited by 5 publications

(3 citation statements)

References 27 publications

(46 reference statements)

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“…Multi-Scale Decomposition [59]: The image is subjected to a multi-scale decomposition using discrete wavelet transforms. This process generates a set of coefficients that represent the image's content at different scales.…”

Section: Discrete Wavelet Analysis For Image Segmentationmentioning

confidence: 99%

Discrete Wavelet Analysis: A Mighty Approach for Image Segmentation

Meng Wu,

Hou

2023

EAI Endorsed Trans e-Learn

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This paper explores the application of Discrete Wavelet Analysis, a mathematical and signal processing technique, in the context of image segmentation, which provide a pixel-level or region-level decomposition of the image, enabling the extraction of relevant information for subsequent analysis and interpretation. Introducing the basic image segmentation techniques and the DWA, this paper discovers that DWA has found widespread application in fields such as signal processing, image analysis, and data compression. Compared with Fourier Transform, DWA is more suitable for image segmentation, having unique advantages and characteristics. Among the procedures of image segmentation, the most important point is feature selection, which determine the criteria for distinguishing different regions within the image. Despite DWA has many advantages, this technology also owns many challenges and limitations, which may be solved by lasting academic research to refine and extend Discrete Wavelet Analysis methodologies for image segmentation. In short, this research highlights the promise of Discrete Wavelet Analysis, emphasizing the use of high- quality image processing.

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Section: Discrete Wavelet Analysis For Image Segmentationmentioning

confidence: 99%

Discrete Wavelet Analysis: A Mighty Approach for Image Segmentation

Meng Wu,

Hou

2023

EAI Endorsed Trans e-Learn

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“…Synthetic aperture radar (SAR) ship detection plays a promising role in many fields such as port management [1][2][3], traffic monitoring [4][5][6], marine surveillance [7][8][9], etc. [10,11], and increasingly becomes an important means for safeguarding maritime rights and interests.…”

Section: Introductionmentioning

confidence: 99%

“…Most anchorfree methods follow the point-prediction fashion: First, locate a pair of keypoints [30,31] or center points [32,33] of objects, and then regress them to final bounding boxes. With the point-prediction fashion, there are several advantages for SAR ship detection: (1) It is friendly to detect small ships as they usually have little semantic information for localization in the high-level features. (2) Ships densely arranged near the shore can be well detected due to the simplification of non-maximum suppression (NMS) [28,34].…”

Section: Introductionmentioning

confidence: 99%

ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images

et al. 2022

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Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on the whole image. However, most pre-defined anchor boxes are redundantly and randomly tiled on the image, manifested as low-quality object proposals. To address these issues, this paper proposes a novel detection method combined with two feature enhancement modules to improve ship detection capability. First, we propose a flexible anchor-free detector (AFD) to generate fewer but higher-quality proposals around the object centers in a keypoint prediction manner, which completely avoids the complicated computation in RPN, such as calculating overlapping related to anchor boxes. Second, we leverage the proposed spatial insertion attention (SIA) module to enhance the feature discrimination between ship targets and background interference. It accordingly encourages the detector to pay attention to the localization accuracy of ship targets. Third, a novel weighted cascade feature fusion (WCFF) module is proposed to adaptively aggregate multi-scale semantic features and thus help the detector boost the detection performance of multi-scale ships in complex scenes. Finally, combining the newly-designed AFD and SIA/WCFF modules, we present a new detector, named anchor-free two-stage ship detector (ATSD), for SAR ship detection under complex background interference. Extensive experiments on two public datasets, i.e., SSDD and HRSID, verify that our ATSD delivers state-of-the-art detection performance over conventional detectors.

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Offshore Bridge Detection in Polarimetric SAR Images Based on Water Network Construction Using Markov Tree

Liu

Yang

Ou³

et al. 2022

Remote Sensing

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It is difficult to detect bridges in synthetic aperture radar (SAR) images due to the inherent speckle noise of SAR images, the interference generated by strong coastal scatterers, and the diversity of bridge and coastal terrain morphologies. In this paper, we present a two-step bridge detection method for polarimetric SAR imagery, in which the probability graph model of a Markov tree is used to build the water network, and bridges are detected by traversing the graph of the water network to determine all adjacent water branch pairs. In the step of the water network construction, candidate water branches are first extracted by using a region-based level set segmentation method. The water network is then built globally as a tree by connecting the extracted water branches based on the probabilistic graph model of a Markov tree, in which a node denotes a single branch and an edge denotes the connection of two adjacent branches. In the step of the bridge detection, all adjacent water branch pairs related to bridges are searched by traversing the constructed tree. Each bridge is finally detected by merging the two contours of the corresponding branch pair. Three polarimetric SAR data acquired by RADARSAT-2 covering Singapore and Lingshui, China, and by TerraSAR-X covering Singapore, are used for testing. The experimental results show that the detection rate, the false alarm rate, and the intersection over union (IoU) between the recognized bridge body and the ground truth are all improved by using the proposed method, compared to the method that constructs a water network based on water branches merging by contour distance.

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An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

Cited by 5 publications

References 27 publications

Discrete Wavelet Analysis: A Mighty Approach for Image Segmentation

Discrete Wavelet Analysis: A Mighty Approach for Image Segmentation

ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images

Offshore Bridge Detection in Polarimetric SAR Images Based on Water Network Construction Using Markov Tree

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