53 research outputs found
The InflateSAR Campaign: Testing SAR Vessel Detection Systems for Refugee Rubber Inflatables
Countless numbers of people lost their lives at Europeβs southern borders in recent years in the attempt to cross to Europe in small rubber inflatables. This work examines satellite-based approaches to build up future systems that can automatically detect those boats. We compare the performance of several automatic vessel detectors using real synthetic aperture radar (SAR) data from X-band and C-band sensors on TerraSAR-X and Sentinel-1. The data was collected in an experimental campaign where an empty boat lies on a lakeβs surface to analyse the influence of main sensor parameters (incidence angle, polarization mode, spatial resolution) on the detectability of our inflatable. All detectors are implemented with a moving window and use local clutter statistics from the adjacent water surface. Among tested detectors are well-known intensity-based (CA-CFAR), sublook-based (sublook correlation) and polarimetric-based (PWF, PMF, PNF, entropy, symmetry and iDPolRAD) approaches. Additionally, we introduced a new version of the volume detecting iDPolRAD aimed at detecting surface anomalies and compare two approaches to combine the volume and the surface in one algorithm, producing two new highly performing detectors. The results are compared with receiver operating characteristic (ROC) curves, enabling us to compare detectors independently of threshold selection
State of the Art of Radar Images Recognition of Surface Ships by Means of Space Monitoring
ΠΠΎΡΡΡΠΏΠΈΠ»Π°: 01.02.2024. ΠΡΠΈΠ½ΡΡΠ° Π² ΠΏΠ΅ΡΠ°ΡΡ: 01.03.2024.Received: 01.02.2024. Accepted: 01.03.2024.ΠΡΠΎΠ±Π»Π΅ΠΌΠ° ΡΠΈΠ½ΡΠ΅Π·Π° ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎ-ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ
ΡΠ΅Π»Π΅ΠΉ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΡΡΠ΅Π΄ΡΡΠ²Π°ΠΌΠΈ ΠΊΠΎΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π°, Π±ΡΠ»Π° ΠΈ ΠΎΡΡΠ°Π΅ΡΡΡ ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ· Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΠΌΡΡ
ΠΊΠ°ΠΊ Ρ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
, ΡΠ°ΠΊ ΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠ·ΠΈΡΠΈΠΉ Π΄Π»Ρ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΡ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ ΠΌΠΎΡΠ΅ΠΏΠ»Π°Π²Π°Π½ΠΈΡ, ΠΊΠΎΠ½ΡΡΠΎΠ»Ρ Π·Π° Π½Π΅Π·Π°ΠΊΠΎΠ½Π½ΠΎΠΉ Π΄ΠΎΠ±ΡΡΠ΅ΠΉ ΡΡΠ±Ρ, ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΊΡΠΈΠ·ΠΈΡΠ½ΡΠΌΠΈ ΡΠΈΡΡΠ°ΡΠΈΡΠΌΠΈ, ΡΠ°ΠΊΠΈΠΌΠΈ ΠΊΠ°ΠΊ Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ Π±Π΅Π΄ΡΡΠ²ΠΈΡ, ΠΌΠΈΠ³ΡΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΏΠΎΡΠΎΠΊΠΈ ΠΈ Π΄ΡΡΠ³ΠΈΠ΅. ΠΠ΄Π½ΠΈΠΌ ΠΈΠ· Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΡ
ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠΉ Π½Π°Π·Π²Π°Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ Π½Π°Π΄Π²ΠΎΠ΄Π½ΡΡ
ΠΊΠΎΡΠ°Π±Π»Π΅ΠΉ, ΠΊΠΎΡΠΎΡΠΎΠΌΡ ΠΈ ΠΏΠΎΡΠ²ΡΡΠ΅Π½ Π΄Π°Π½Π½ΡΠΉ ΠΎΠ±Π·ΠΎΡ, Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π½ΡΠΉ ΠΏΠΎ ΠΈΠ½ΠΎΡΡΡΠ°Π½Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠ°ΠΌ. Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΠΉ ΠΎΠ±Π·ΠΎΡ, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΠΉ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ ΠΏΠΎΠ΄ΡΠΎΠ±Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΠ΅ΡΠ΅Π½ΠΈΡ Π½Π°Π·Π²Π°Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΡ
ΡΠΈΡΠΎΠΊΠΈΠΌ ΠΊΡΡΠ³ΠΎΠΌ Π°Π²ΡΠΎΡΠΎΠ² Π² ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ Π΄Π΅ΡΡΡΠΈΠ»Π΅ΡΠΈΡ, Π±ΡΠ΄Π΅Ρ ΠΏΠΎΠ»Π΅Π·Π΅Π½ ΡΠΎΠ·Π΄Π°ΡΠ΅Π»ΡΠΌ ΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠΌ ΡΡΠ΅Π΄ΡΡΠ² ΠΊΠΎΡΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ Π·Π° ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ΠΌ ΠΌΠΎΡΡΠΊΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ.The issue of synthesizing and analyzing algorithms of processing radar images of spatially distributed targets, obtained through space monitoring tools, remains one of the most significant both theoretically and practically. This is particularly crucial for ensuring maritime safety, monitoring illegal fishing activities, and managing crisis situations such as natural disasters and migration flows. One of the most common applications of this problem is the recognition of surface ships, to which this review is devoted. The review is performed using foreign materials. Thus, the proposed review, which includes a detailed analysis of contemporary methods addressing the mentioned challenges, proposed by a wide range of authors over the past decades, will be valuable for developers and researchers in the field of space observation of marine surface conditions
Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar
Constant false alarm rate (CFAR) algorithms using a local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of the targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently, a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this article, a polarimetric whitening filter TS CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for PolSAR imagery. The CFAR detector uses a polarimetric whitening filter (PWF) to turn the multidimensional problem to a 1-D case. It uses truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require prior knowledge of the interfering targets, and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves the detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations, and the results are in line with the theory
Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements
This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics
Scalable computing for earth observation - Application on Sea Ice analysis
In recent years, Deep learning (DL) networks have shown considerable improvements and have become a preferred methodology in many different applications. These networks have outperformed other classical techniques, particularly in large data settings. In earth observation from the satellite field, for example, DL algorithms have demonstrated the ability to learn complicated nonlinear relationships in input data accurately. Thus, it contributed to advancement in this field. However, the training process of these networks has heavy computational overheads. The reason is two-fold: The sizable complexity of these networks and the high number of training samples needed to learn all parameters comprising these architectures. Although the quantity of training data enhances the accuracy of the trained models in general, the computational cost may restrict the amount of analysis that can be done. This issue is particularly critical in satellite remote sensing, where a myriad of satellites generate an enormous amount of data daily, and acquiring in-situ ground truth for building a large training dataset is a fundamental prerequisite.
This dissertation considers various aspects of deep learning based sea ice monitoring from SAR data. In this application, labeling data is very costly and time-consuming. Also, in some cases, it is not even achievable due to challenges in establishing the required domain knowledge, specifically when it comes to monitoring Arctic Sea ice with Synthetic Aperture Radar (SAR), which is the application domain of this thesis. Because the Arctic is remote, has long dark seasons, and has a very dynamic weather system, the collection of reliable in-situ data is very demanding. In addition to the challenges of interpreting SAR data of sea ice, this issue makes SAR-based sea ice analysis with DL networks a complicated process.
We propose novel DL methods to cope with the problems of scarce training data and address the computational cost of the training process. We analyze DL network capabilities based on self-designed architectures and learn strategies, such as transfer learning for sea ice classification. We also address the scarcity of training data by proposing a novel deep semi-supervised learning method based on SAR data for incorporating unlabeled data information into the training process. Finally, a new distributed DL method that can be used in a semi-supervised manner is proposed to address the computational complexity of deep neural network training
Β Ocean Remote Sensing with Synthetic Aperture Radar
The ocean covers approximately 71% of the Earthβs surface, 90% of the biosphere and contains 97% of Earthβs water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This bookβProgress in SAR Oceanographyβprovides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objectsβ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography
Bayesian super-resolution with application to radar target recognition
This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target
recognition algorithms require access to a database of previously recorded or
synthesized radar images for the targets of interest, or a database of features
based on those images. However, the resolution of a new image acquired under
non-ideal conditions may not be as good as that of the images used to generate
the database. Therefore it is proposed to use super-resolution techniques to
match the resolution of new images with the resolution of database images.
A comprehensive review of the literature is given for super-resolution when
used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain
Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It
is shown that the Bayesian approach improves the probability of correct target
classification over standard super-resolution techniques.
The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety
of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the
super-resolution algorithm is then tested as part of a Bayesian target recognition
framework using measured radar data
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