scholarly journals On-Board Ship Detection for Medium Resolution Optical Sensors

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3062
Author(s):  
Somnath Ghosh ◽  
Pramod Kumar Konugurthi ◽  
Gowri Shankar Rao Singupurapu ◽  
Shivi Patel ◽  
Tirupathi Tammanagari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
False Alarm ◽  
Optical Sensors ◽  
Detection System ◽  
Wide Field ◽  
Constant False Alarm Rate ◽  
Ship Detection ◽  
Medium Resolution ◽  
Power Radiation ◽  
Board Ship

In recent years there has been an increased interest in ocean surveillance. The activity includes control and monitoring of illegal fisheries, manmade ocean pollution and illegal sea traffic surveillance, etc. The key problem is how to identify ships and ship-like objects accurately and in a timely manner. In this context, currently, many solutions have been proposed based on high resolution optical and radar remote sensing systems. Most often, these systems suffer from two major limitations viz., limited swath, thereby requiring multiple satellites to cover the region of interest and huge volumes of data being transmitted to ground, even though effective per-pixel information content is minimal. Another limitation is that the existing systems are either simulated on ground or built using the non-space qualified/Commercial Of-The-Shelf (COTS) components. This paper proposes an efficient on-board ship detection system/package connected with medium resolution wide swath optical camera. The methodology adopted has three major components, viz., onboard data processing for improving the radiometric fidelity, followed by a ship detection using modified Constant False Alarm Rate algorithm (CFAR) and a false alarm suppression module to mask false identifications. Finally, the package outputs only the locations of the ships, which is transmitted to the ground. The proposed system reduces the effective volume of data to be transmitted and processed on ground and also significantly cuts down the turnaround time for achieving the end objective. The system is built on radiation hardened Field Programmable Gate Array (FPGA) devices to meet the various engineering constraints such as real-time performance, limited onboard power, radiation hardness, handling of multiple custom interfaces etc. The system is tested with one of the medium resolution Multispectral Visual and Near Infra-Red (MX-VNIR) sensor having a spatial resolution of around 50 m and swath of around 500 Kms, which would be flown with one of the upcoming satellites. The systems performance is also verified on ground with Indian Remote Sensing (IRS) Satellite’s Resourcesat’s Advanced Wide Field Sensor (AWiFS) data and the results are found to be quite encouraging as well as meeting the mission objectives.

scholarly journals Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2536 ◽  
Author(s):  
Jian He ◽  
Yongfei Guo ◽  
Hangfei Yuan
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Detection Efficiency ◽  
Detection System ◽  
Automatic Detection ◽  
Sea Surface ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Saliency Model

Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.

scholarly journals AMARO—An On-Board Ship Detection and Real-Time Information System

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1324 ◽  
Author(s):  
Katharina Willburger ◽  
Kurt Schwenk ◽  
Jörg Brauchle
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Detection System ◽  
Image Data ◽  
Prototype System ◽  
Communication Link ◽  
Remotely Sensed Data ◽  
Product Data ◽  
Ship Detection ◽  
Board Ship

The monitoring of worldwide ship traffic is a field of high topicality. Activities like piracy, ocean dumping, and refugee transportation are in the news every day. The detection of ships in remotely sensed data from airplanes, drones, or spacecraft contributes to maritime situational awareness. However, the crucial factor is the up-to-dateness of the extracted information. With ground-based processing, the time between image acquisition and delivery of the extracted product data is in the range of several hours, mainly due to the time consumed by storing and transmission of the large image data. By processing and analyzing them on-board and transmitting the product data directly as ship position, heading, and velocity, the delay can be shortened to some minutes. Real-time connections via satellite telecommunication services allow small packets of information to be sent directly to the user without significant delay. The AMARO (Autonomous Real-Time Detection of Moving Maritime Objects) project at DLR is a feasibility study of an on-board ship detection system involving on-board processing and real-time communication. The operation of a prototype system was successfully demonstrated on an airborne platform in spring 2018. The on-ground user could be informed about detected vessels within minutes after sighting without a direct communication link. In this article, the scope, aim, and design of the AMARO system are described, and the results of the flight experiment are presented in detail.

scholarly journals Multi-Spectral Ship Detection Using Optical, Hyperspectral, and Microwave SAR Remote Sensing Data in Coastal Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 4064 ◽  
Author(s):  
Kyung-Ae Park ◽  
Jae-Jin Park ◽  
Jae-Cheol Jang ◽  
Ji-Hyun Lee ◽  
Sangwoo Oh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Satellite Images ◽  
Coastal Region ◽  
Probability Of Detection ◽  
Constant False Alarm Rate ◽  
Coastal Regions ◽  
Ship Detection ◽  
Radar Images ◽  
Sar Remote Sensing

The necessity of efficient monitoring of ships in coastal regions has been increasing over time. Multi-satellite observations make it possible to effectively monitor vessels. This study presents the results of ship detection methodology, applied to optical, hyperspectral, and microwave satellite images in the seas around the Korean Peninsula. Spectral matching algorithms are used to detect ships using hyperspectral images with hundreds of spectral channels and investigate the similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from the backscattering coefficients of Sentinel-1B SAR and ALOS-2 PALSAR2 images. Validation results exhibited that the locations of the satellite-detected vessels showed good agreement with real-time location data within the Sentinel-1B coverage in the Korean coastal region. This study presented the probability of detection values of optical and SAR-based ship detection and discussed potential causes of the errors. This study also suggested a possibility for real-time operational use of vessel detection from multi-satellite images based on optical, hyperspectral, and SAR remote sensing, particularly in the inaccessible coastal regions off North Korea, for comprehensive coastal management and sustainability.

scholarly journals Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data

2020 ◽  
Vol 12 (1) ◽  
pp. 1169-1184
Author(s):  
Liang Zhong ◽  
Xiaosheng Liu ◽  
Peng Yang ◽  
Rizhi Lin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Infrared Imaging ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Application Potential ◽  
Nighttime Light

AbstractNighttime light remote sensing images show significant application potential in marine ship monitoring, but in areas where ships are densely distributed, the detection accuracy of the current methods is still limited. This article considered the LJ1-01 data as an example, compared with the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data, and explored the application of high-resolution nighttime light images in marine ship detection. The radiation values of the aforementioned two images were corrected to achieve consistency, and the interference light sources of the ship light were filtered. Then, when the threshold segmentation and two-parameter constant false alarm rate methods are combined, the ships’ location information was with obtained, and the reliability of the results was analyzed. The results show that the LJ1-01 data can not only record more potential ship light but also distinguish the ship light and background noise in the data. The detection accuracy of the LJ1-01 data in both ship detection methods is significantly higher than that of the NPP/VIIRS data. This study analyzes the characteristics, performance, and application potential of the high-resolution nighttime light data in the detection of marine vessels. The relevant results can provide a reference for the high-precision monitoring of nighttime marine ships.

scholarly journals ESTABLISHMENT OF VICARIOUS CALIBRATION/VALIDATION FACILITY FOR SPACE BORNE HIGH AND MID RESOLUTION OPTICAL SENSORS

2018 ◽  
Vol XLII-5 ◽  
pp. 913-919
Author(s):  
B. Santhi Sree ◽  
N. Raghavender ◽  
K. S. Raju ◽  
D. Chandrasekaran ◽  
B. Gopala Krishna ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Radiometric Calibration ◽  
Vicarious Calibration ◽  
The Earth ◽  
Medium Resolution ◽  
Infra Red ◽  
Materials Used ◽  
Edge Based ◽  
Periodic Evaluation

<p><strong>Abstract.</strong> Vicarious calibration refers to methods that make use of “invariant” natural targets of the Earth for the post-launch calibration of sensors. This process of calibration is useful for initial phase orbit operation of sensor and thereafter for validation during its operational span. This method of periodic evaluation of radiometric and geometric performance of the space-borne optical sensors and validation of derived radiance/reflectance is to ensure availability of consistent and accurate data products to the user community. This paper describes one such Cal/Val facility’s design, engineering aspects and realization at National Remote Sensing Centre (NRSC), Shadnagar. The target materials used for filling the Cal/Val site are studied for its reflectance. These targets have reflectance ranging from 9% to 53%, in the VNIR (Visible and Near Infra-Red) region. This paper also describes instruments used for calibration and homogeneity studies of targets for its invariance since operationalisation (January 2016). The last section of this paper is about reflectance based absolute radiometric calibration of medium resolution Indian Remote Sensing (IRS) sensor (Resourcesat and Cartosat series) using RT model and edge based LSF/MTF estimation of high resolution sensor carried out using the Cal/Val facility.</p>

scholarly journals On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component

2019 ◽  
Vol 11 (7) ◽  
pp. 762 ◽  
Author(s):  
Yuan Yao ◽  
Zhiguo Jiang ◽  
Haopeng Zhang ◽  
Yu Zhou
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Performance ◽  
Detection Algorithm ◽  
Remote Sensing Images ◽  
Detection Scheme ◽  
Ship Detection ◽  
Computing Platform ◽  
Low Efficiency ◽  
Board Ship

Micro-nano satellites have provided a large amount of remote sensing images for many earth observation applications. However, the hysteresis of satellite-ground mutual communication of massive remote sensing images and the low efficiency of traditional information processing flow have become the bottlenecks for the further development of micro-nano satellites. To solve this problem, this paper proposes an on-board ship detection scheme based on deep learning and Commercial Off-The-Shelf (COTS) component, which can be used to achieve near real-time on-board processing by micro-nano satellite computing platform. The on-board ship detection algorithm based on deep learning consists of a feature extraction network, Region Proposal Network (RPN) with square anchors, Global Average Pooling (GAP), and Bigger-Left Non-Maximum Suppression (BL-NMS). With the help of high performance COTS components, the proposed scheme can extract target patches and valuable information from remote sensing images quickly and accurately. A ground demonstration and verification system is built to verify the feasibility and effectiveness of our scheme. Our method achieves the performance with 95.9% recall and 80.5% precision in our dataset. Experimental results show that the scheme has a good application prospect in micro-nano satellites with limited power and computing resources.

scholarly journals Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

2019 ◽  
Vol 11 (13) ◽  
pp. 1529 ◽  
Author(s):  
Chao Dong ◽  
Jinghong Liu ◽  
Fang Xu ◽  
Chenglong Liu
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Optical Remote Sensing ◽  
Scale Analysis ◽  
Ship Detection ◽  
Multi Scale ◽  
Saliency Maps ◽  
Multi Scale Analysis

Automatic ship detection by Unmanned Airborne Vehicles (UAVs) and satellites is one of the fundamental challenges in maritime research due to the variable appearances of ships and complex sea backgrounds. To address this issue, in this paper, a novel multi-level ship detection algorithm is proposed to detect various types of offshore ships more precisely and quickly under all possible imaging variations. Our object detection system consists of two phases. First, in the category-independent region proposal phase, the steerable pyramid for multi-scale analysis is performed to generate a set of saliency maps in which the candidate region pixels are assigned to high salient values. Then, the set of saliency maps is used for constructing the graph-based segmentation, which can produce more accurate candidate regions compared with the threshold segmentation. More importantly, the proposed algorithm can produce a rather smaller set of candidates in comparison with the classical sliding window object detection paradigm or the other region proposal algorithms. Second, in the target identification phase, a rotation-invariant descriptor, which combines the histogram of oriented gradients (HOG) cells and the Fourier basis together, is investigated to distinguish between ships and non-ships. Meanwhile, the main direction of the ship can also be estimated in this phase. The overall algorithm can account for large variations in scale and rotation. Experiments on optical remote sensing (ORS) images demonstrate the effectiveness and robustness of our detection system.

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