Saliency based algorithm for ship detection in infrared images

Abstract

In this paper we have worked on the problem of automatic ship detection in IR images. Segmentation of IR ship images is always a challenging task because of the intensity inhomogeneity, sea clutters and noise. In our proposed approach, we have shown that efficiency and accuracy of IR ship detection algorithms can be enhanced by only searching around the salient parts of the input image. In order to identify most salient regions, at first we computed the saliency map of the input image using the Graph-Based Visual Saliency (GBVS) algorithm. Next a multilevel thresholding of the saliency map is performed to get the ranked salient regions of the input image. By using the ship size as prior information, top-k regions are further processed to get the fine segmentation of the target. For this purpose we have used spatial constraint based fuzzy c-mean (FCM) segmentation algorithm along with a strategy to choose the cluster selection threshold. Experiments are performed on a data set of 18 diverse and challenging IR ship images, collected from different sources. Results show that our proposed framework is very effective and perform better compare to the methods which directly search the target in entire image.

A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments

“…Visible Chen [12], Cane [13], Marie [14], Chen [15], Chen [16], Liu [17], Shan [18], Gal [19], Lin [20], Chen [21], Lee [22], Feng [23], Shan [24], Fefilatyev [25] IR N/A Tang [26], Liu [27], Hu [28], Lin [29] MWIR Özertem [30], Wang [31] LWIR Sun [32], Lu [33], Bai [34], Leira [35], Bai [36], Mumtaz [37], Singh [38], Zhang [39], Xu [40], Zhou [41], Schöller [42], Li [43], Westlake [44] Visible + IR N/A Islam [45], Wei [46] Visible + MWIR Nita [47] Visible + LWIR Zhang [48], Ribeiro [49], Farahnakian [50] Visible + SWIR + LWIR Stets [51] Visible + SWIR + MWIR + LWIR Bouma [52] Visible: refers to visible-band images; IR: infrared images, including short-wave infrared (SWIR), medium-wave infrared (MWIR), and long-wave infrared (LWIR); N/A, means that the type of image was not specified in the paper. from the statistical data that infrared images are used the most, followed by visible images.…”

“…Liu [27] proposed an improved light nonlocal depth feature (NLDF) for low-contrast infrared images. Mumtaz [37] used graph-based visual saliency (GBVS). Lin [20] fused a variety of visual features, such as gradient texture, brightness, and color features, to obtain a salient image.…”

“…Li [43], Liu [27], Mumtaz [37], Lin [20] (1) Suitable for thermal infrared images; (2) It works well for low-contrast images.…”

Infrared ship detection based on latent low-rank decomposition and salient feature fusion

“…And the local spatial information is refined by Markov random field. In the literature [3], the saliency detection method is introduced into infrared ship detection, and an infrared ship target segmentation algorithm based on GBVS (Graph Based Visual Saliency) combined with FCM (Fuzzy C-means) is proposed. In this method, the GBVS-based saliency feature extraction algorithm is first used to obtain the global saliency graph and thus extract the convex hull in the region of interest.…”

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

“…The human visual system-based methods are based on the local contrast measure and selective attention mechanism of the ship target region, and therefore, the feature saliency map calculation of infrared ship target is the foremost topic for HVS-based methods. Mumtaz et al [22] adopted graph-based visual saliency (GBVS) method to compute a saliency map, and then extracted the ship target by using multilevel thresholding of the saliency map. The GBVS-based method may extract strongly salient clutter regions and fail to detect real ship target when the sea clutter is heavy.…”