Ship Detection for High-Resolution SAR Images Based on Feature Analysis

Wang, Chao; Jiang, Shaofeng; Zhang, Hong; Wu, Fan; Zhang, Bo

doi:10.1109/lgrs.2013.2248118

Cited by 93 publications

(58 citation statements)

References 8 publications

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“…Most methods exploit the fact that ships on SAR images are much brighter than the sea surface due to the corner-reflection effect [6,7]. That is, the ship is a target having a higher backscatter coefficient, and hence the ship is very bright in SAR imagery, while the sea surface is very dark in SAR imagery because it has a lower backscatter coefficient.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery

et al. 2017

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For ship detection, X-band synthetic aperture radar (SAR) imagery provides very useful data, in that ship targets look much brighter than surrounding sea clutter due to the corner-reflection effect. However, there are many phenomena which bring out false detection in the SAR image, such as noise of background, ghost phenomena, side-lobe effects and so on. Therefore, when ship-detection algorithms are carried out, we should consider these effects and mitigate them to acquire a better result. In this paper, we propose an efficient method to detect ship targets from X-band Kompsat-5 SAR imagery using the artificial neural network (ANN). The method produces the ship-probability map using ANN, and then detects ships from the ship-probability map by using a threshold value. For the purpose of getting an improved ship detection, we strived to produce optimal input layers used for ANN. In order to reduce phenomena related to the false detections, the non-local (NL)-means filter and median filter were utilized. The NL-means filter effectively reduced noise on SAR imagery without smoothing edges of the objects, and the median filter was used to remove ship targets in SAR imagery. Through the filtering approaches, we generated two input layers from a Kompsat-5 SAR image, and created a ship-probability map via ANN from the two input layers. When the threshold value of 0.67 was imposed on the ship-probability map, the result of ship detection from the ship-probability map was a 93.9% recall, 98.7% precision and 6.1% false alarm rate. Therefore, the proposed method was successfully applied to the ship detection from the Kompsat-5 SAR image.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery

et al. 2017

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“…Therefore, more and more learning methods can be used to classify the SAR images with different characteristics [9,10].…”

Section: Introductionmentioning

confidence: 99%

A New Method Based on Two-Stage Detection Mechanism for Detecting Ships in High-Resolution SAR Images

Xiong

et al. 2017

MATEC Web Conf.

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Abstract. Ship detection in synthetic aperture radar (SAR) remote sensing images, being a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Aiming at the requirements of ship detection in high-resolution SAR images, the accuracy, the intelligent level, a better real-time operation and processing efficiency, The characteristics of ocean background and ship target in high-resolution SAR images were analyzed, we put forward a ship detection algorithm in high-resolution SAR images. The algorithm consists of two detection stages: The first step designs a pre-training classifier based on improved spectral residual visual model to obtain the visual salient regions containing ship targets quickly, then achieve the purpose of probably detection of ships. In the second stage, considering the Bayesian theory of binary hypothesis detection, a local maximum posterior probability (MAP) classifier is designed for the classification of pixels. After the parameter estimation and judgment criterion, the classification of pixels are carried out in the target areas to achieve the classification of two types of pixels in the salient regions. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of detection methods. Comparing with classical CFAR detection algorithms, experimental results show that the algorithm can achieve a better effect of suppressing false alarms, which caused by the speckle noise and ocean clutter background inhomogeneity. At the same time, the detection speed is increased by 25% to 45%.

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“…The presence of distinguishable sidelobes in azimuth and range can also potentially cause multiple areas of bright and dark texture, leading to multiple detections for a single target [2].…”

Section: Introductionmentioning

confidence: 99%

“…Hybrid approaches include Meyer's fusion of CFAR detectors and wavelet analysis [7] as well as Wang's method of fast block CFAR combined with feature analysis [2]. Leng et al [8] employ a bilateral CFAR algorithm acting on a combination of the intensity and spatial distribution of the image, while Hou et al [9] use an iterative Log-normal CFAR combined with background subtraction at each stage.…”

Section: Introductionmentioning

confidence: 99%