scholarly journals A Multi-Feature and Multi-Level Matching Algorithm Using Aerial Image and AIS for Vessel Identification

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1317 ◽  
Author(s):  
Supu Xiu ◽  
Yuanqiao Wen ◽  
Haiwen Yuan ◽  
Changshi Xiao ◽  
Wenqiang Zhan ◽  
...  
Keyword(s):  
Field Experiments ◽  
Aerial Images ◽  
Aerial Image ◽  
Automatic Identification ◽  
Identification System ◽  
Accurate Identification ◽  
Matching Algorithm ◽  
Surface Vessels ◽  
Aerial Vehicle ◽  
Multi Level

In order to monitor and manage vessels in channels effectively, identification and tracking are very necessary. This work developed a maritime unmanned aerial vehicle (Mar-UAV) system equipped with a high-resolution camera and an Automatic Identification System (AIS). A multi-feature and multi-level matching algorithm using the spatiotemporal characteristics of aerial images and AIS information was proposed to detect and identify field vessels. Specifically, multi-feature information, including position, scale, heading, speed, etc., are used to match between real-time image and AIS message. Additionally, the matching algorithm is divided into two levels, point matching and trajectory matching, for the accurate identification of surface vessels. Through such a matching algorithm, the Mar-UAV system is able to automatically identify the vessel’s vision, which improves the autonomy of the UAV in maritime tasks. The multi-feature and multi-level matching algorithm has been employed for the developed Mar-UAV system, and some field experiments have been implemented in the Yangzi River. The results indicated that the proposed matching algorithm and the Mar-UAV system are very significant for achieving autonomous maritime supervision.

Verification of AIS Data by using Video Images taken by a UAV

2019 ◽  
Vol 72 (06) ◽  
pp. 1345-1358 ◽  
Author(s):  
Fan Zhou ◽  
Shengda Pan ◽  
Jingjing Jiang
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Target Image ◽  
Inland Waterways ◽  
Safe Navigation ◽  
Traffic Regulation ◽  
Video Images ◽  
Aerial Vehicle ◽  
Positioning Algorithm ◽  
Vibe Algorithm

Effective technical methods for verifying the authenticity and accuracy of Automatic Identification System (AIS) data, which are important for safe navigation and traffic regulation, are still lacking. In this study, we propose a new method to verify AIS data by using video images taken by an Unmanned Aerial Vehicle (UAV). An improved ViBe algorithm is used to extract the ship target image from the video images and the ship's spatial position is calculated using a monocular target-positioning algorithm. The positioning results are compared with the position, speed and course data of the same ship in AIS, and the authenticity and accuracy of the AIS data are verified. The results of the experiment conducted in the inland waterways of Huangpu River in Shanghai, China, show that AIS signals can be automatically checked and verified by a UAV in real time and can thus improve the supervision efficiency of maritime departments.

scholarly journals Line Matching Algorithm for Aerial Image Combining image and object space similarity constraints

2016 ◽  
Vol XLI-B3 ◽  
pp. 783-788
Author(s):  
Jingxue Wang ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Zhenyu Cao ◽  
Hong Zhu ◽  
...  
Keyword(s):  
Aerial Images ◽  
Aerial Image ◽  
Object Image ◽  
Expansion Process ◽  
Object Plane ◽  
Matching Method ◽  
Matching Algorithm ◽  
Image Mapping ◽  
Straight Line ◽  
Image Object

A new straight line matching method for aerial images is proposed in this paper. Compared to previous works, similarity constraints combining radiometric information in image and geometry attributes in object plane are employed in these methods. Firstly, initial candidate lines and the elevation values of lines projection plane are determined by corresponding points in neighborhoods of reference lines. Secondly, project reference line and candidate lines back forward onto the plane, and then similarity measure constraints are enforced to reduce the number of candidates and to determine the finial corresponding lines in a hierarchical way. Thirdly, "one-to-many" and "many-to-one" matching results are transformed into "one-to-one" by merging many lines into the new one, and the errors are eliminated simultaneously. Finally, endpoints of corresponding lines are detected by line expansion process combing with "image-object-image" mapping mode. Experimental results show that the proposed algorithm can be able to obtain reliable line matching results for aerial images.

scholarly journals Line Matching Algorithm for Aerial Image Combining image and object space similarity constraints

2016 ◽  
Vol XLI-B3 ◽  
pp. 783-788
Author(s):  
Jingxue Wang ◽  
Weixi Wang ◽  
Xiaoming Li ◽  
Zhenyu Cao ◽  
Hong Zhu ◽  
...  
Keyword(s):  
Aerial Images ◽  
Aerial Image ◽  
Object Image ◽  
Expansion Process ◽  
Object Plane ◽  
Matching Method ◽  
Matching Algorithm ◽  
Image Mapping ◽  
Straight Line ◽  
Image Object

A new straight line matching method for aerial images is proposed in this paper. Compared to previous works, similarity constraints combining radiometric information in image and geometry attributes in object plane are employed in these methods. Firstly, initial candidate lines and the elevation values of lines projection plane are determined by corresponding points in neighborhoods of reference lines. Secondly, project reference line and candidate lines back forward onto the plane, and then similarity measure constraints are enforced to reduce the number of candidates and to determine the finial corresponding lines in a hierarchical way. Thirdly, "one-to-many" and "many-to-one" matching results are transformed into "one-to-one" by merging many lines into the new one, and the errors are eliminated simultaneously. Finally, endpoints of corresponding lines are detected by line expansion process combing with "image-object-image" mapping mode. Experimental results show that the proposed algorithm can be able to obtain reliable line matching results for aerial images.

Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection

2019 ◽  
Vol 15 (8) ◽  
pp. 769-776
Author(s):  
Kalaivani Subramani ◽  
Shantharajah Periyasamy ◽  
Padma Theagarajan
Keyword(s):  
Pattern Matching ◽  
Error Rate ◽  
Median Filter ◽  
Identification System ◽  
Double Line ◽  
Cultivation System ◽  
Matching Algorithm ◽  
Non Local ◽  
Mining Methods ◽  
Blight Disease

Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

Automatic Identification System Receiver for Small Fishing Vessels

2020 ◽  
Author(s):  
Febus Reidj G. Cruz ◽  
Jeremiah A. Ordiales ◽  
Malvin Angelo C. Reyes ◽  
Pinky T. Salvanera
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Fishing Vessels
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