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 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 A Deep Multiscale Fusion Method via Low-Rank Sparse Decomposition for Object Saliency Detection Based on Urban Data in Optical Remote Sensing Images

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Cheng Zhang ◽  
Dan He
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Saliency Detection ◽  
Saliency Map ◽  
Low Rank ◽  
Optical Remote Sensing ◽  
Fusion Method ◽  
Remote Sensing Images ◽  
Sparse Decomposition ◽  
Depth Feature

The urban data provides a wealth of information that can support the life and work for people. In this work, we research the object saliency detection in optical remote sensing images, which is conducive to the interpretation of urban scenes. Saliency detection selects the regions with important information in the remote sensing images, which severely imitates the human visual system. It plays a powerful role in other image processing. It has successfully made great achievements in change detection, object tracking, temperature reversal, and other tasks. The traditional method has some disadvantages such as poor robustness and high computational complexity. Therefore, this paper proposes a deep multiscale fusion method via low-rank sparse decomposition for object saliency detection in optical remote sensing images. First, we execute multiscale segmentation for remote sensing images. Then, we calculate the saliency value, and the proposal region is generated. The superpixel blocks of the remaining proposal regions of the segmentation map are input into the convolutional neural network. By extracting the depth feature, the saliency value is calculated and the proposal regions are updated. The feature transformation matrix is obtained based on the gradient descent method, and the high-level semantic prior knowledge is obtained by using the convolutional neural network. The process is iterated continuously to obtain the saliency map at each scale. The low-rank sparse decomposition of the transformed matrix is carried out by robust principal component analysis. Finally, the weight cellular automata method is utilized to fuse the multiscale saliency graphs and the saliency map calculated according to the sparse noise obtained by decomposition. Meanwhile, the object priors knowledge can filter most of the background information, reduce unnecessary depth feature extraction, and meaningfully improve the saliency detection rate. The experiment results show that the proposed method can effectively improve the detection effect compared to other deep learning methods.

Multi-Visual Features Based Ship Detection in Remote Sensing Images

2014 ◽  
Vol 643 ◽  
pp. 228-232
Author(s):  
Xiao Guang Hu ◽  
Cheng Qi Cheng ◽  
De Ren Li
Keyword(s):  
Remote Sensing ◽  
Detection Method ◽  
Saliency Map ◽  
Visual Features ◽  
Remote Sensing Images ◽  
Complex Scene ◽  
Detection Experiment ◽  
Ship Detection ◽  
Visual Contrast ◽  
Design Idea

In this paper, we propose a novel ship detection method based on multi-visual features after analyzing the characteristics of ship in the sea. According to the principal of the visual contrast, brightness and orientation saliency map of ship object are respectively generated, and then they are integrated to obtain the total saliency map. In addition to the brightness and orientation of the ship objects, the method doesn’t use other prior knowledge of them. In ship detection experiment, the experimental results prove our method can effectively concentrate on the ship objects regardless of their size and brightness, and thereby improve the capacity of visual attention in complex scene. Thus, the design idea of our method is verified.

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 for Optical Remote Sensing Images Based on Visual Attention Enhanced Network

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2271 ◽  
Author(s):  
Fukun Bi ◽  
Jinyuan Hou ◽  
Liang Chen ◽  
Zhihua Yang ◽  
Yanping Wang
Keyword(s):  
Remote Sensing ◽  
Visual Attention ◽  
False Alarm ◽  
Detection Method ◽  
State Of The Art ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Local Candidate

Ship detection plays a significant role in military and civil fields. Although some state-of-the-art detection methods, based on convolutional neural networks (CNN) have certain advantages, they still cannot solve the challenge well, including the large size of images, complex scene structure, a large amount of false alarm interference, and inshore ships. This paper proposes a ship detection method from optical remote sensing images, based on visual attention enhanced network. To effectively reduce false alarm in non-ship area and improve the detection efficiency from remote sensing images, we developed a light-weight local candidate scene network( L 2 CSN) to extract the local candidate scenes with ships. Then, for the selected local candidate scenes, we propose a ship detection method, based on the visual attention DSOD(VA-DSOD). Here, to enhance the detection performance and positioning accuracy of inshore ships, we both extract semantic features, based on DSOD and embed a visual attention enhanced network in DSOD to extract the visual features. We test the detection method on a large number of typical remote sensing datasets, which consist of Google Earth images and GaoFen-2 images. We regard the state-of-the-art method [sliding window DSOD (SW+DSOD)] as a baseline, which achieves the average precision (AP) of 82.33%. The AP of the proposed method increases by 7.53%. The detection and location performance of our proposed method outperforms the baseline in complex remote sensing scenes.

scholarly journals S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 423-430 ◽  
Author(s):  
Ruiqian Zhang ◽  
Jian Yao ◽  
Kao Zhang ◽  
Chen Feng ◽  
Jiadong Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Detection Method ◽  
Saliency Detection ◽  
Head Model ◽  
Shape Features ◽  
Remote Sensing Images ◽  
Line Segments ◽  
Ship Detection ◽  
Efficient Detection

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

scholarly journals S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 423-430 ◽  
Author(s):  
Ruiqian Zhang ◽  
Jian Yao ◽  
Kao Zhang ◽  
Chen Feng ◽  
Jiadong Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Detection Method ◽  
Saliency Detection ◽  
Head Model ◽  
Shape Features ◽  
Remote Sensing Images ◽  
Line Segments ◽  
Ship Detection ◽  
Efficient Detection

Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

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