Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy

Huo, Weibo; Huang, Yulin; Pei, Jifang; Zhang, Qian; Gu, Qin; Yang, Jianyu

doi:10.3390/s18041196

Cited by 46 publications

(22 citation statements)

References 31 publications

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“…Various advances in research and various works have been recently presented that deal with the problem of irregular immigration, [8][9][10][11][12]. These works make use of various image processing techniques for the detection of people and boats in the sea, demonstrating that it is feasible to use technology in combination with UAV systems to face these problems.…”

Section: Introductionmentioning

confidence: 99%

Secure UAV‐Based System to Detect Small Boats Using Neural Networks

et al. 2019

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This work presents a system to detect small boats (pateras) to help tackle the problem of this type of perilous immigration. The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. The use of this algorithm improves current detection systems based on image processing through the application of filters thanks to the fact that the network learns to distinguish the aforementioned objects through patterns without depending on where they are located. The main result of the proposal has been a classifier that works in real time, allowing the detection of pateras and people (who may need to be rescued), kilometres away from the coast. This could be very useful for Search and Rescue teams in order to plan a rescue before an emergency occurs. Given the high sensitivity of the managed information, the proposed system includes cryptographic protocols to protect the security of communications.

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Section: Introductionmentioning

confidence: 99%

Secure UAV‐Based System to Detect Small Boats Using Neural Networks

et al. 2019

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“…The Shannon definition of entropy has two interpretations. First, it can be regarded as a measure of information given by the random variable or the experiment under consideration [43]; second, it can be viewed as a measure of uncertainty of a random variable or experiment [44]. When it is considered before conducting the experiment, then entropy measures the uncertainty related to the results of the analysis.…”

Section: Definition the Shannon Entropy [42] Of A Discrete Random Vamentioning

confidence: 99%

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

Buṣu

Bușu

2018

Processes

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In this paper we propose a methodology to study circular economy processes based on mathematical modelling. In open-ended systems, waste could be converted back to recycling, transforming the economy from linear to circular. The concept of entropy and the second law of thermodynamics give the argument for a scale reduction of material circulation. As humans extract more and more energy and matter for the economy, the degree of entropy is likely to increase. Based on the findings of economic studies on the implications of industrialization in the case of growing economies, this study aims at evaluating circular economy processes at the European Union (EU) level using a Shannon-Entropy-based algorithm. An entropy-based analysis was conducted for the 28 European Union countries during the time frame 2007–2016. The modelling process consists of constructing a composite indicator which is composed of a weighted sum of all indicators developed by an algorithm based on Shannon Entropy. The weights assigned to each indicator in our analysis measure the significance of each indicator involved in the development of the composite indicator. The results are similar to the international rakings, consolidating and confirming the accuracy and reliability of this approach.

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“…The ship detection methods used in SAR images are usually inherited from the optical remote sensing domain. The detection methods can be divided into four types [ 8 , 9 , 10 ]: (1) template matching-based object detection methods; (2) knowledge-based object detection methods; (3) object-based image analysis (OBIA)-based object detection methods; and (4) machine learning-based object detection methods. Machine learning-based methods usually have better performance compared with the other three detectors.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Ship Detection and Orientation Estimation in SAR Images Based on Attention Module and Angle Regression

Wang

Jiang

2018

Sensors

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Ship detection and angle estimation in SAR images play an important role in marine surveillance. Previous works have detected ships first and estimated their orientations second. This is time-consuming and tedious. In order to solve the problems above, we attempt to combine these two tasks using a convolutional neural network so that ships may be detected and their orientations estimated simultaneously. The proposed method is based on the original SSD (Single Shot Detector), but using a rotatable bounding box. This method can learn and predict the class, location, and angle information of ships using only one forward computation. The generated oriented bounding box is much tighter than the traditional bounding box and is robust to background disturbances. We develop a semantic aggregation method which fuses features in a top-down way. This method can provide abundant location and semantic information, which is helpful for classification and location. We adopt the attention module for the six prediction layers. It can adaptively select meaningful features and neglect weak ones. This is helpful for detecting small ships. Multi-orientation anchors are designed with different sizes, aspect ratios, and orientations. These can consider both speed and accuracy. Angular regression is embedded into the existing bounding box regression module, and thus the angle prediction is output with the position and score, without requiring too many extra computations. The loss function with angular regression is used for optimizing the model. AAP (average angle precision) is used for evaluating the performance. The experiments on the dataset demonstrate the effectiveness of our method.

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Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy

Cited by 46 publications

References 31 publications

Secure UAV‐Based System to Detect Small Boats Using Neural Networks

Secure UAV‐Based System to Detect Small Boats Using Neural Networks

Modeling the Circular Economy Processes at the EU Level Using an Evaluation Algorithm Based on Shannon Entropy

Simultaneous Ship Detection and Orientation Estimation in SAR Images Based on Attention Module and Angle Regression

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