On the relationship between SAS image resolution and target-detection performance

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

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Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar

Remote Sensing ◽

10.3390/rs13040701 ◽

2021 ◽

Vol 13 (4) ◽

pp. 701 ◽

Cited By ~ 1

Author(s):

Binbin Wang ◽

Hao Cha ◽

Zibo Zhou ◽

Bin Tian

Keyword(s):

Fourier Transform ◽

Target Detection ◽

Time Integration ◽

Detection Performance ◽

Bistatic Radar ◽

Detection Range ◽

Robust Detection ◽

Coherent Integration ◽

Long Time ◽

Long Time Integration

Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method.

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Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

Remote Sensing ◽

10.3390/rs13091854 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1854

Author(s):

Syed Muhammad Arsalan Bashir ◽

Yi Wang

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Resolution Enhancement ◽

Super Resolution ◽

Detection Performance ◽

Image Resolution ◽

Small Object ◽

Remote Sensing Images ◽

Feature Aggregation ◽

Image Super Resolution

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16.

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Fast Implementation of Insect Multi-Target Detection Based on Multimodal Optimization

Remote Sensing ◽

10.3390/rs13040594 ◽

2021 ◽

Vol 13 (4) ◽

pp. 594

Author(s):

Rui Wang ◽

Yiming Zhang ◽

Weiming Tian ◽

Jiong Cai ◽

Cheng Hu ◽

...

Keyword(s):

Target Detection ◽

Computational Cost ◽

Detection Performance ◽

Coherent Detection ◽

Observation Data ◽

Multimodal Optimization ◽

Performance Simulation ◽

Single Target ◽

Parameter Search ◽

Long Time

Entomological radars are important for scientific research of insect migration and early warning of migratory pests. However, insects are hard to detect because of their tiny size and highly maneuvering trajectory. Generalized Radon–Fourier transform (GRFT) has been proposed for effective weak maneuvering target detection by long-time coherent detection via jointly motion parameter search, but the heavy computational burden makes it impractical in real signal processing. Particle swarm optimization (PSO) has been used to achieve GRFT detection by fast heuristic parameter search, but it suffers from obvious detection probability loss and is only suitable for single target detection. In this paper, we convert the realization of GRFT into a multimodal optimization problem for insect multi-target detection. A novel niching method without radius parameter is proposed to detect unevenly distributed insect targets. Species reset and boundary constraint strategy are used to improve the detection performance. Simulation analyses of detection performance and computational cost are given to prove the effectiveness of the proposed method. Furthermore, real observation data acquired from a Ku-band entomological radar is used to test this method. The results show that it has better performance on detected target amount and track continuity in insect multi-target detection.

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Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Sensors ◽

10.3390/s20113270 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3270 ◽

Cited By ~ 1

Author(s):

Baris Satar ◽

Gokhan Soysal ◽

Xue Jiang ◽

Murat Efe ◽

Thiagalingam Kirubarajan

Keyword(s):

Target Detection ◽

Impulsive Noise ◽

Simulated Data ◽

Real Data ◽

Detection Performance ◽

Passive Radar ◽

Mobile Telecommunication ◽

Telecommunication System ◽

Conventional Methods ◽

Non Gaussian

Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted l 1 and l 2 norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with α stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.

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The effect of spatial resolution on hyperspectral target detection performance

10.1117/12.631313 ◽

2005 ◽

Cited By ~ 3

Author(s):

T. Skauli ◽

I. Kåsen

Keyword(s):

Target Detection ◽

Spatial Resolution ◽

Detection Performance

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Human target detection performance using synthetic-aperture radar imagery in a quasi-operational context

Optical Engineering ◽

10.1117/1.2798530 ◽

2007 ◽

Vol 46 (10) ◽

pp. 106402

Author(s):

Gary J. Ewing

Keyword(s):

Synthetic Aperture Radar ◽

Target Detection ◽

Detection Performance ◽

Synthetic Aperture ◽

Radar Imagery ◽

Synthetic Aperture Radar Imagery ◽

Aperture Radar

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The Effects of Cue Utilization and Target-Related Information on Target Detection during a Simulated Drone Search and Rescue Task

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931218621053 ◽

2018 ◽

Vol 62 (1) ◽

pp. 227-231

Author(s):

Shaheen Shekh ◽

Jaime C. Auton ◽

Mark W. Wiggins

Keyword(s):

Target Detection ◽

Undergraduate Students ◽

Search And Rescue ◽

Target Information ◽

Improve Performance ◽

Cognitive Demands ◽

Cue Utilization ◽

Related Information ◽

Bus Driver ◽

The Relationship

Remotely piloted vehicles or ‘drones’ have become ubiquitous both privately and commercially. One of the numerous applications for drones involves the search and rescue for specified targets. The use of ‘cues’ during target detection has been shown to improve performance and reduce cognitive demands in many environments. This study examined the relationship between cue utilization and level of target detail during a high-fidelity simulated drone search and rescue task. Seventy-six undergraduate students from an Australian University operated a payload (long range camera) to detect a ‘target’ (a bus driver stranded in the Utah desert) while flying on a pre-programmed flight path. The results indicated that the provision of detailed target information was associated with greater rates of target detection. Further, participants with higher cue utilization were more likely to locate the target. Finally, participants with higher cue utilization, and provided with basic target information, were more likely to locate the target than participants with lower cue utilization. The practical and theoretical implications of the outcomes are discussed.

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