A New Ship Detection Method for Massive Data High-Resolution Remote Sensing Images

2012 ◽  
Vol 532-533 ◽  
pp. 1105-1109 ◽  
Author(s):  
Hao Sun ◽  
Yu Li ◽  
Ge Liu ◽  
Hui Long ◽  
Hong Qi Wang
Keyword(s):  
Remote Sensing ◽  
Detection Method ◽  
Learning Algorithm ◽  
Structural Information ◽  
New Method ◽  
Massive Data ◽  
Good Precision ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Multi Scale

This paper proposes a new method for automatic ship targets detection in remote sensing images. The method uses adaptive segmentation algorithm for getting possible ship targets first, and then calculates Histograms of Oriented Gradient (HOG) feature to extract the structural information of ships, followed by supervised learning algorithm to identify the possible ship targets. Multi-scale sliding-window is used to handle targets with different scales. The experimental results prove that this new method has a good precision and robustness for most of the ship targets and give attention to the efficiency.

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.

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 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.

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