scholarly journals Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

2020 ◽  
Vol 12 (20) ◽  
pp. 3370
Author(s):  
Jianming Hu ◽  
Xiyang Zhi ◽  
Wei Zhang ◽  
Longfei Ren ◽  
Lorenzo Bruzzone
Keyword(s):  
Remote Sensing ◽  
Weighting Function ◽  
Image Interpretation ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Filter Function ◽  
Ship Detection ◽  
Background Prior

Automatic ship detection in complicated maritime background is a challenging task in the field of optical remote sensing image interpretation and analysis. In this paper, we propose a novel and reliable ship detection framework based on a visual saliency model, which can efficiently detect multiple targets of different scales in complex scenes with sea clutter, clouds, wake and islands interferences. Firstly, we present a reliable background prior extraction method adaptive for the random locations of targets by computing boundary probability and then generate a saliency map based on the background prior. Secondly, we compute the prior probability of salient foreground regions and propose a weighting function to constrain false foreground clutter, gaining the foreground-based prediction map. Thirdly, we integrate the two prediction maps and improve the details of the integrated map by a guided filter function and a wake adjustment function, obtaining the fine selection of candidate regions. Afterwards, a classification is further performed to reduce false alarms and produce the final ship detection results. Qualitative and quantitative evaluations on two public available datasets demonstrate the robustness and efficiency of the proposed method against four advanced baseline methods.

scholarly journals Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description

2020 ◽  
Vol 12 (1) ◽  
pp. 152 ◽  
Author(s):  
Ting Nie ◽  
Xiyu Han ◽  
Bin He ◽  
Xiansheng Li ◽  
Hongxing Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Saliency Map ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection

Ship detection in panchromatic optical remote sensing images is faced with two major challenges, locating candidate regions from complex backgrounds quickly and describing ships effectively to reduce false alarms. Here, a practical method was proposed to solve these issues. Firstly, we constructed a novel visual saliency detection method based on a hyper-complex Fourier transform of a quaternion to locate regions of interest (ROIs), which can improve the accuracy of the subsequent discrimination process for panchromatic images, compared with the phase spectrum quaternary Fourier transform (PQFT) method. In addition, the Gaussian filtering of different scales was performed on the transformed result to synthesize the best saliency map. An adaptive method based on GrabCut was then used for binary segmentation to extract candidate positions. With respect to the discrimination stage, a rotation-invariant modified local binary pattern (LBP) description was achieved by combining shape, texture, and moment invariant features to describe the ship targets more powerfully. Finally, the false alarms were eliminated through SVM training. The experimental results on panchromatic optical remote sensing images demonstrated that the presented saliency model under various indicators is superior, and the proposed ship detection method is accurate and fast with high robustness, based on detailed comparisons to existing efforts.

scholarly journals A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

2020 ◽  
Vol 12 (19) ◽  
pp. 3115 ◽  
Author(s):  
Liqiong Chen ◽  
Wenxuan Shi ◽  
Cien Fan ◽  
Lian Zou ◽  
Dexiang Deng
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
False Alarms ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Optical Remote Sensing ◽  
Dense Network ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Coarse To Fine

Automatic ship detection in optical remote sensing images is of great significance due to its broad applications in maritime security and fishery control. Most ship detection algorithms utilize a single-band image to design low-level and hand-crafted features, which are easily influenced by interference like clouds and strong waves and not robust for large-scale variation of ships. In this paper, we propose a novel coarse-to-fine ship detection method based on discrete wavelet transform (DWT) and a deep residual dense network (DRDN) to address these problems. First, multi-spectral images are adopted for sea-land segmentation, and an enhanced DWT is employed to quickly extract ship candidate regions with missing alarms as low as possible. Second, panchromatic images with clear spatial details are used for ship classification. Specifically, we propose the local residual dense block (LRDB) to fully extract semantic feature via local residual connection and densely connected convolutional layers. DRDN mainly consists of four LRDBs and is designed to further remove false alarms. Furthermore, we exploit the multiclass classification strategy, which can overcome the large intra-class difference of targets and identify ships of different sizes. Extensive experiments demonstrate that the proposed method has high robustness in complex image backgrounds and achieves higher detection accuracy than other state-of-the-art methods.

scholarly journals A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images

2020 ◽  
Vol 12 (2) ◽  
pp. 246 ◽  
Author(s):  
Yue Wu ◽  
Wenping Ma ◽  
Maoguo Gong ◽  
Zhuangfei Bai ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Detection Methods ◽  
False Alarms ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Detection Strategy ◽  
Image Pixels ◽  
Bounding Boxes ◽  
Coarse To Fine

With the increasing resolution of optical remote sensing images, ship detection in optical remote sensing images has attracted a lot of research interests. The current ship detection methods usually adopt the coarse-to-fine detection strategy, which firstly extracts low-level and manual features, and then performs multi-step training. Inadequacies of this strategy are that it would produce complex calculation, false detection on land and difficulty in detecting the small size ship. Aiming at these problems, a sea-land separation algorithm that combines gradient information and gray information is applied to avoid false alarms on land, the feature pyramid network (FPN) is used to achieve small ship detection, and a multi-scale detection strategy is proposed to achieve ship detection with different degrees of refinement. Then the feature extraction structure is adopted to fuse different hierarchical features to improve the representation ability of features. Finally, we propose a new coarse-to-fine ship detection network (CF-SDN) that directly achieves an end-to-end mapping from image pixels to bounding boxes with confidences. A coarse-to-fine detection strategy is applied to improve the classification ability of the network. Experimental results on optical remote sensing image set indicate that the proposed method outperforms the other excellent detection algorithms and achieves good detection performance on images including some small-sized ships and dense ships near the port.

Automated Ship Detection from Optical Remote Sensing Images

2012 ◽  
Vol 500 ◽  
pp. 785-791 ◽  
Author(s):  
Yin Dong Yu ◽  
Xu Bo Yang ◽  
Shuang Jiu Xiao ◽  
Jia Le Lin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
High Speed ◽  
Saliency Map ◽  
Work Flow ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Optical Images ◽  
Region Growth

Automatic ship detection from remote sensing images is very important as a variant of applications such as harbor management, cargo shipping, marine rescue and naval warfare will call for the aids of the analysis of these images. This paper focuses on the processing of space-born optical images (SDSOI). With the continuous development of photography technology, high-resolution remote sensing images are produced with extremely high speed, but still lack of an effective and swift method to automatically process them and get an applicable result. The whole work flow is based on three modules. First, separating land and sea with threshold segmentation, texture segmentation and region-growth and hollow-filling algorithm, and extract the sea region as ROI. Second, apply contrast box algorithm to the ROI to get the candidates of targets. Thirdly, use shape analysis to delete some simple false candidates, and use the saliency map algorithm to eliminate possible influence of clouds. Experimental results of a series of optical remote sensing images captured by satellites indicate that our approach is effective and swift in dealing with high resolution SDSOI, obtains a satisfactory ship detection miss rate and alarm rate.

scholarly journals Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning

2017 ◽  
Vol 12 ◽  
pp. 05012 ◽  
Author(s):  
Ying Liu ◽  
Hong-Yuan Cui ◽  
Zheng Kuang ◽  
Guo-Qing Li
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection
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