Fast target detection method for high-resolution SAR images based on variance weighted information entropy

Cao, Zongjie; Ge, Yong; Feng, Jilan

doi:10.1186/1687-6180-2014-45

Cited by 22 publications

(11 citation statements)

References 13 publications

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“…Subsequently, the variance WIE concept is proposed and firstly applied to the ROIs extraction of infrared images. Due to the fact that objects with different radiation or reflection usually appear to have distinct gray value in real images, the variance WIE is proved to be an effective way to evaluate the complex degree of intensity distribution upon an image robustly [9,11]. For a 256 gray levels image, the variance WIE of the image is defined as follows:…”

Section: Variance Wiementioning

confidence: 99%

Adaptive ship detection in SAR images using variance WIE-based method

Wang

Chen

2016

SIViP

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Automatic detection of ship targets from synthetic aperture radar (SAR) images is an important and challenging problem. Given the different nature of target returns in homogeneous and heterogeneous regions in SAR imagery, conventional detection algorithms fail to yield automatic and robust results. In support of automatic vessel monitoring, an adaptive detection framework designed for detecting ships from SAR imagery is proposed in this paper, and the variance weighted information entropy is introduced into the framework construction. Experimental results indicate that the proposed method can effectively detect the ship targets from various circumstances without any prior knowledge.

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Section: Variance Wiementioning

confidence: 99%

Adaptive ship detection in SAR images using variance WIE-based method

Wang

Chen

2016

SIViP

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“…To describe the distinction mentioned above, statistical characteristics have been investigated and proved to be powerful descriptions in remote sensing image processing. For instance, the variance weighted information entropy (WIE) was applied to detect target both in infrared and SAR images and achieved impressive performance [22,23]. In this paper, the value of region variances from optical remote sensing images in their uniform areas, the area including false alarms, and the partial area of ship target is primarily tested.…”

Section: The Proposed Saliency Modelmentioning

confidence: 99%

“…Examples of saliency maps with comparison to the 4 state-of-the-art methods. From left to right: input image, Itti [9], GBVS(Graph-Based Visual saliency) [10], SR(Spectral-Residual) [13], COV(Covariance saliency) [23], and our method. Our method outperforms other typical methods visually.…”

Section: Input Imagementioning

confidence: 99%

“…Our method outperforms other typical methods visually. Figure 8 presents the results of our saliency approach and other typical models including the Itti [9], GBVS (Graph-Based Visual saliency) [10], SR (Spectral-Residual) [13] and COV (Covariance saliency) [23] methods with respect to some sample images from our dataset. These images can be divided into several types based on the different complex backgrounds, such as thin and thick cloud cover, the interference of islands, fog, sea clutter, etc.…”

Section: Input Imagementioning

confidence: 99%

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Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor

Dong

Liu

2018

Remote Sensing

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Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main coarse-to-fine stages: prescreening and discrimination. In the prescreening stage, we construct a novel visual saliency detection method according to the difference of statistical characteristics between highly non-uniform regions which allude to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions. In this way, not only can the targets be precisely detected, but false alarms are also significantly reduced. In the discrimination stage, to get a better representation of the target, both shape and texture features characterizing the ship target are extracted and concatenated as a feature vector for subsequent classification. Moreover, the combined feature is invariant to the rotation. Finally, a trainable Gaussian support vector machine (SVM) classifier is performed to validate real ships out of ship candidates. We demonstrate the superior performance of the proposed hierarchical detection method with detailed comparisons to existing efforts.

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“…As for the detection method based on information, Cao et al [11] proposed a fast target detection method for high-resolution SAR images based on variance weighted information entropy (VWIE), and Wang and Chen [12] have used the method based on multiscale variance weighted image entropy (MVWIE) method to detect the ship targets in the complex background SAR images. They are proved to be effective to detect the true targets successfully; however, the discrimination of the target and the surrounding cells is difficult, and there are usually some false alarm targets in the detection result.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the ship target detection in high-resolution SAR images based on information theory and Harris corner detection

Deng

Wang

Liu

et al. 2018

J Wireless Com Network

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In order to make up the shortcomings of some existing ship target detection algorithms for high-resolution synthetic aperture radar (SAR) images, a ship target detection algorithm based on information theory and Harris corner detection for SAR images is proposed in this paper. Firstly, the SAR image is pretreated, and next, it is divided into superpixel patches by using the improved simple linear iterative clustering (SLIC) superpixel generation algorithm. Then, the self-information value of the superpixel patches is calculated, and the threshold T 1 is set to select the candidate superpixel patches. And then, the extended neighborhood weighted information entropy growth rate threshold T 2 is set to eliminate the false alarm candidate superpixel patches. Finally, the Harris corner detection algorithm is used to process the detection result and the number of the corner threshold T 3 is set to filter out the false alarm patches, and the final SAR image target detection result is obtained. The effectiveness and superiority of the proposed algorithm are verified by comparing the proposed method with the results of constant false alarm rate (CFAR) detection algorithm combined with morphological processing algorithm and other ship target detection algorithms.

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Fast target detection method for high-resolution SAR images based on variance weighted information entropy

Cited by 22 publications

References 13 publications

Adaptive ship detection in SAR images using variance WIE-based method

Adaptive ship detection in SAR images using variance WIE-based method

Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor

Analysis of the ship target detection in high-resolution SAR images based on information theory and Harris corner detection

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