Target detection in complex scene of SAR image based on existence probability
© The Author(s). 2016
Received: 30 April 2016
Accepted: 27 October 2016
Published: 8 November 2016
This study proposes a target detection approach based on the target existence probability in complex scenes of a synthetic aperture radar image. Superpixels are the basic unit throughout the approach and are labelled into each classified scene by a texture feature. The original and predicted saliency depth values for each scene are derived through self-information of all the labelled superpixels in each scene. Thereafter, the target existence probability is estimated based on the comparison of two saliency depth values. Lastly, an improved visual attention algorithm, in which the scenes of the saliency map are endowed with different weights related to the existence probabilities, derives the target detection result. This algorithm enhances the attention for the scene that contains the target. Hence, the proposed approach is self-adapting for complex scenes and the algorithm is substantially suitable for different detection missions as well (e.g. vehicle, ship or aircraft detection in the related scenes of road, harbour or airport, respectively). Experimental results on various data show the effectiveness of the proposed method.
KeywordsTarget detection SAR image Self-information Superpixel Complex scene
Target detection in complex scenes, such as urban areas, airports or harbours, is a challenge in the area of synthetic aperture radar (SAR) image interpretation. Instead of a single scene, such as grassland, farmland or sea, the target detection performance in complex scenes is degraded by using conventional methods. In these complex scenes, the clutter produced by the background may be similar with the targets and are detected as false alarms. For example, the strong reflections of urban building considerably affect vehicle detection. Moreover, the echo waves from various backgrounds overlap and induce strong coherent speckles .
To date, many algorithms are adaptive for detecting a specific target in a complex scene by exploring the region of interest (ROI). For these specific target detections (e.g. ship, vehicle and aircraft detections) in a complex scene, the algorithms obtain the ROIs (e.g. ocean, road and airport) by utilising a preprocess module [2–10]. In [2–5], ROIs are obtained by combining the region mask, which is derived using geographic information system (GIS) data and image data. However, the performances of the GIS data-based algorithms are profoundly influenced by the accuracy of the GIS information. These data often suffer from systematic or random positional errors; hence, the region axes have to be realigned to the image . Wang et al. used the Markov random field (MRF) algorithm to extract the ocean scene and detect ships . In , the authors achieve the elimination of land areas by filtering the divided sub-image based on the rate of high-intensity pixels. In , the SAR image is segmented into N sub-images or regions that comprise high, median or low backscatters based on the k-means programme. The pixels of the targets are detected by the thresholds of the different regions. For these segmentation- or classification-based algorithms, ROI and its characteristics are defined by human experience. That is, the type of target and the possible scenes where the targets exist are predefined before detection. This way, only one algorithm suits one type of target; hence, the range of application and efficiency of the algorithm are unsatisfying.
This study proposes an existence probability-based approach for the SAR image target detection in a complex scene. The existence probability takes the advantage of saliency depth (SD) value to represent the probability that targets exist in a scene. Prior to the estimation of these probabilities, a preprocessing module exists to obtain scenes and arrange the labels of superpixels. Accordingly, an improved visual attention detection algorithm achieves the detection result. The proposed algorithm is self-adapting for complex scenes and for different types of target in the SAR image target detection. The results of the simulated and real SAR data experiments verify the performance of the proposed algorithm.
2.1 Preprocessing module
The estimation and detection modules are processed in superpixel elements. Therefore, the generation of superpixels is achieved in the preprocessing module. Compared with a pixel, a superpixel has substantial statistical characteristic, which is the basis for calculating the saliency depth value. Furthermore, the observed object in the visual attention model is replaced by the superpixel from the pixel; hence, the single salient pixels are contained into the un-salient superpixel. In the current study, the superpixels are generated by the simple linear iterative clustering (SLIC) method  because they process with limited computational effort and the superpixels adhere to the boundaries well.
In the preprocessing module, each scene that has an approximate background is extracted using the classification algorithm. A dense texture feature extracted through a morphological operation , which has been proven suitable for remote-sensing image classification, is opted. Six morphological operations are used in the extraction of features, including opening, closing, opening and closing by reconstruction and opening and closing by top-hat. Meanwhile, the structural elements in the morphological operation comprise square or diamond shapes, and the scales are set at 3 or 7, respectively.
2.2 Estimation module
The existence probability of the target is estimated through an SD value of each classified scene, thereby presenting the possibility that a single scene contains the targets. After extracting the outlier information in the scene, the estimation measures original and predicted saliencies. The original SD value of the scene means the existence probability of the scene with potential targets, whereas the predicted SD value derived after excluding the outliers means the probability without targets. Thereafter, a comparison between the two SD values shows the existence probability of the targets.
2.2.1 Self-information of the superpixel
In a relatively homogeneous scene, the intensity distributions of the entire scene are similar to that of most of the superpixels in the scene. In addition, for an SAR image that needs detection, the number of target pixels is limited and the distribution of the target is different from the scene. Therefore, the target superpixels are discriminated by measuring the similarity of distributions between the scene and the superpixels.
Variable sp is the superpixel, variable s is the scene and the “|” symbol stands for conditional. After all, the similarity between the superpixel and the scene S(sp,s) is inversely proportional to its self-information I(sp|s).
The first and second factors indicate the texture and Gaussian distribution components, respectively. The texture in one superpixel was assumed homogenous based on the SLIC algorithm. Therefore, the texture component in (3) can be regard as constant, and then, the pixels in the superpixel could be considered iid.
Variable P(SP ij ) is the probability of superpixel SP ij . The probability is equal to the accumulation of the conditional probability of each pixel SP ij (r) in the superpixel.
Variable t is a constant that is experimentally set as [2,3]; thus, the number of outliers are sufficient to represent the salient level of s i ; variables μ i and δ i are the mean and variance, respectively, that are calculated over all the self-information values of the superpixels in s i .
2.2.2 SD-value-based existence probability
Variable D i is the new SD value after excluding the outliers in scene s i , and α is a constant predictive coefficient. The difference between the original and predicted SD value interprets the existence probability of the targets in a scene. When the original value is outstanding beyond the predicted value, the scene is supposed to contain targets. Lastly, the scenes that lack targets are censored, whereas the rates between the differences of the scene with the targets are assigned as weights of the saliency map in the next detection module.
2.3 Detection module
Following the idea of saliency in the estimation module, an improved superpixel-based visual attention model is proposed for the SAR image target detection. The proposed model is based on the Itti visual attention model , which is commonly used for the salient object detection in an optical image. The structure of the proposed model is shown in the red box of Fig. 1.
The variable k p is the number of residual eigenvalue in the matrix Σ(p). The initial value of the variables are set as follows: I(1) = I, Σ(1) = Σ and k 1 = Rank(I). The variable f is the degree of the low-rank approximation, which is experimentally set as [0.3, 0.7].
The second improvement is the important information-enhancing component. In the Itti model, after the Gaussian pyramid model, a centre-surround difference component based on the subtraction between the different resolution images extracts the difference, thereby indicating the value in the saliency map. However, the pixels around the targets in the result of the image subtraction are insufficient to be detected. In the SVD-based pyramid, with the increasing layer of the pyramid, the image information is decreasing, whereas the principle component (e.g. the target region) is retained. The important information is enhanced by adding different layers of the SVD pyramid.
In (13), the saliency of the isolated pixels is averaged into its surrounding, whereas the target superpixels or scene superpixels are homogeneous and have limited influence.
Generally, the saliency map of the proposed model is weighted by the existence probability. The saliency of the high existence probability scene is enhanced whilst the others are depressed; thus, the weighted map reorders the priority of the visual attention detection. With the improved visual attention model, the target detection module is suitable for the SAR image target detection.
3 Experimental results
Figure 4b shows the distributions of the self-information value belonging to the five scenes without the targets. Figure 4e, h, k, n show the distributions of the scene with the following targets: grass, bush, road and both bush and road scenes, respectively, and the self-information values of the targets are marked by the red dotted circle in the four figures. By comparing Fig. 4e, h, k, n with Fig. 4b, we can find that the self-information value of the targets are all larger than the self-information value of the backgrounds. Figure 4c shows the original and predicted SD values of all scenes. The colours of the predicted values are displayed in the legend alongside the figures, and the original values are identical cyan coloured. From Fig. 4f, only the original SD value of the grass scene is beyond the predicted value, thereby satisfying the fact in Fig. 4d that the targets are added in the grass scene. A similar situation is shown in Fig. 4i, l, o. Particularly, Fig. 4c shows that no target exists; thus, all original values are lower than the predicted values. By contrast, Fig. 4o shows that two groups of targets appear at the bush and road scenes and the corresponding original values are larger than the predictions. Hence, the existence of targets can make the SD value larger, and the module achieves correct results no matter the target situation or situation where the targets are located in multi-scenes.
In this study, the target detection in the complex scene SAR image was focused by estimating the existence probability of the target in each scene. A texture-based classification is used to obtain the scenes firstly. Thereafter, the SD value based on information theory is used to estimate the existence probability. Lastly, an improved visual attention detection module is used to derive the detection result. The proposed method is a superpixel-based approach that maximises the extensive statistical features provided by the superpixel in the estimation module and decreases the false alarm rate in the detection module. By the target existence probability, the focus of visual attention is changed to the scene that contains the target superpixels with high possibility. With these benefits, the proposed approach is suitable for target detection in the complex scene SAR image and is extensively used for different target detection missions.
The authors declare that they have no competing interests.
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