Special Issue "Remote Sensing for Target Object Detection and Identification"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 October 2019).

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Gemine Vivone
E-Mail Website
Guest Editor
Institute of Methodologies for Environmental Analysis, CNR-IMAA, 85050 Tito Scalo, Italy
Interests: data fusion; statistical signal processing; detection; tracking
Special Issues and Collections in MDPI journals
Prof. Dr. Paolo Addesso
E-Mail Website
Guest Editor
DIEM, University of Salerno, 84084 Fisciano, Italy
Interests: remote sensing; image processing; signal processing; sequential Bayesian estimation; estimation theory; detection theory; statistical signal processing; fractal models; data fusion; gravitational waves; localization; nonlinear devices; sensor networks
Dr. Amanda Ziemann
E-Mail Website
Guest Editor
Los Alamos National Laboratory, New Mexico, USA
Interests: Hyperspectral Imaging; Target Detection; Anomaly Detection

Special Issue Information

Dear Colleagues,

Target object detection and identification is one of the main tasks for a remote sensing system and is of paramount importance in several fields, including environmental and urban monitoring, geological research, hazard and disaster management, and emergency/security and military applications. Moreover, the huge amount of data acquired nowadays by sensors on-board satellite platforms allows for the development of new methodologies to properly address this specific remote sensing task.

Thus, this Special Issue focuses on presenting the latest advances and trends for target object detection and identification within the wide field of remote sensing. Both methodological/theoretical and application-oriented papers are welcome. This Special Issue will cover (but will not be limited to) the following topics:

  • Target object detection, tracking, and identification for the environmental monitoring of the atmosphere (e.g., cloud and plume detection/identification);
  • Target object detection, tracking, and identification for the environmental monitoring of the sea (e.g., sea ice monitoring, iceberg tracking, oil spills);
  • Target object detection, tracking, and identification for security and military applications;
  • Target object detection and identification for urban monitoring (e.g., change detection);
  • Optical and/or infrared data for target object detection and identification;
  • Radar and/or SAR data for target object detection and identification;
  • Multi-sensor (spatio-temporal, spatio-spectral, and multimodal) data fusion for target object detection and identification;
  • Methods, algorithms, and theoretical models for target object detection, tracking, and identification;
  • Machine learning for target object detection, tracking, and identification.
Dr. Gemine Vivone
Dr. Paolo Addesso
Dr. Amanda Ziemann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Remote sensing
  • Data fusion
  • Machine learning
  • Signal processing
  • Detection theory
  • Classification
  • Target tracking

Published Papers (15 papers)

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Editorial

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Open AccessEditorial
Editorial for Special Issue “Remote Sensing for Target Object Detection and Identification”
Remote Sens. 2020, 12(1), 196; https://doi.org/10.3390/rs12010196 - 06 Jan 2020
Cited by 1 | Viewed by 1074
Abstract
This special issue gathers fourteen papers focused on the application of a variety of target object detection and identification techniques for remotely-sensed data. These data are acquired by different types of sensors (both passive and active) and are located on various platforms, ranging [...] Read more.
This special issue gathers fourteen papers focused on the application of a variety of target object detection and identification techniques for remotely-sensed data. These data are acquired by different types of sensors (both passive and active) and are located on various platforms, ranging from satellites to unmanned aerial vehicles. This editorial provides an overview of the contributed papers, briefly presenting the technologies and algorithms employed as well as the related applications. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)

Research

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Open AccessArticle
Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density
Remote Sens. 2019, 11(23), 2831; https://doi.org/10.3390/rs11232831 - 28 Nov 2019
Cited by 4 | Viewed by 852
Abstract
The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is [...] Read more.
The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is impractical; a state-of-the-art method has been developed to dig out valuable information in the temporal domain and separate small-faint targets from background noise. However, there are still two drawbacks: (1) The mixture of Gaussians (MoG) model assumes that noise of different frames satisfies independent and identical distribution (i.i.d.); (2) the assumption of Markov random field (MRF) would fail in more complex noise scenarios. In real scenarios, the noise is actually more complicated than the MoG model. To address this problem, a method using the non-i.i.d. mixture of Gaussians (NMoG) with modified flux density (MFD) is proposed in this paper. We firstly construct a novel data structure containing spatial and temporal information with an infrared image sequence. Then, we use an NMoG model to describe the noise, which can be separated with the background via the variational Bayes algorithm. Finally, we can select the component containing true targets through the obvious difference of target and noise in an MFD maple. Extensive experiments demonstrate that the proposed method performs better in complicated noisy scenarios than the competitive approaches. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessEditor’s ChoiceArticle
Pixel Tracking to Estimate Rivers Water Flow Elevation Using Cosmo-SkyMed Synthetic Aperture Radar Data
Remote Sens. 2019, 11(21), 2574; https://doi.org/10.3390/rs11212574 - 02 Nov 2019
Cited by 5 | Viewed by 1676
Abstract
The lack of availability of historical and reliable river water level information is an issue that can be overcome through the exploitation of modern satellite remote sensing systems. This research has the objective of contributing in solving the information-gap problem of river flow [...] Read more.
The lack of availability of historical and reliable river water level information is an issue that can be overcome through the exploitation of modern satellite remote sensing systems. This research has the objective of contributing in solving the information-gap problem of river flow monitoring through a synthetic aperture radar (SAR) signal processing technique that has the capability to perform water flow elevation estimation. This paper proposes the application of a new method for the design of a robust procedure to track over the time double-bounce reflections from bridges crossing rivers to measure the gap space existing between the river surface and bridges. Specifically, the difference in position between the single and double bounce is suitably measured over the time. Simulated and satellite temporal series of SAR data from COSMO-SkyMed data are compared to the ground measurements recorded for three gauges sites over the Po and Tiber Rivers, Italy. The obtained performance indices confirm the effectiveness of the method in the estimation of water level also in narrow or ungauged rivers. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Infrared Optical Observability of an Earth Entry Orbital Test Vehicle Using Ground-Based Remote Sensors
Remote Sens. 2019, 11(20), 2404; https://doi.org/10.3390/rs11202404 - 16 Oct 2019
Cited by 1 | Viewed by 810
Abstract
Optical design parameters for a ground-based infrared sensor rely strongly on the target’s optical radiation properties. Infrared (IR) optical observability and imaging simulations of an Earth entry vehicle were evaluated using a comprehensive numerical model. Based on a ground-based IR detection system, this [...] Read more.
Optical design parameters for a ground-based infrared sensor rely strongly on the target’s optical radiation properties. Infrared (IR) optical observability and imaging simulations of an Earth entry vehicle were evaluated using a comprehensive numerical model. Based on a ground-based IR detection system, this model considered many physical mechanisms including thermochemical nonequilibrium reacting flow, radiative properties, optical propagation, detection range, atmospheric transmittance, and imaging processes. An orbital test vehicle (OTV) was selected as the research object for analysis of its observability using a ground-based infrared system. IR radiance contours, maximum detecting range (MDR), and thermal infrared (TIR) pixel arrangement were modeled. The results show that the distribution of IR radiance is strongly dependent on the angle of observation and the spectral band. Several special phenomena, including a strong receiving region (SRR), a characteristic attitude, a blind zone, and an equivalent zone, are all found in the varying altitude MDR distributions of mid-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) irradiances. In addition, the possible increase in detectivity can greatly improve the MDR at high altitudes, especially for the backward and forward views. The difference in the peak radiance of the LWIR images is within one order of magnitude, but the difference in that of the MWIR images varies greatly. Analyses and results indicate that this model can provide guidance in the design of remote ground-based detection systems. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Efficient Object Detection Framework and Hardware Architecture for Remote Sensing Images
Remote Sens. 2019, 11(20), 2376; https://doi.org/10.3390/rs11202376 - 13 Oct 2019
Cited by 9 | Viewed by 1169
Abstract
Object detection in remote sensing images on a satellite or aircraft has important economic and military significance and is full of challenges. This task requires not only accurate and efficient algorithms, but also high-performance and low power hardware architecture. However, existing deep learning [...] Read more.
Object detection in remote sensing images on a satellite or aircraft has important economic and military significance and is full of challenges. This task requires not only accurate and efficient algorithms, but also high-performance and low power hardware architecture. However, existing deep learning based object detection algorithms require further optimization in small objects detection, reduced computational complexity and parameter size. Meanwhile, the general-purpose processor cannot achieve better power efficiency, and the previous design of deep learning processor has still potential for mining parallelism. To address these issues, we propose an efficient context-based feature fusion single shot multi-box detector (CBFF-SSD) framework, using lightweight MobileNet as the backbone network to reduce parameters and computational complexity, adding feature fusion units and detecting feature maps to enhance the recognition of small objects and improve detection accuracy. Based on the analysis and optimization of the calculation of each layer in the algorithm, we propose efficient hardware architecture of deep learning processor with multiple neural processing units (NPUs) composed of 2-D processing elements (PEs), which can simultaneously calculate multiple output feature maps. The parallel architecture, hierarchical on-chip storage organization, and the local register are used to achieve parallel processing, sharing and reuse of data, and make the calculation of processor more efficient. Extensive experiments and comprehensive evaluations on the public NWPU VHR-10 dataset and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the proposed framework. Moreover, for evaluating the performance of proposed hardware architecture, we implement it on Xilinx XC7Z100 field programmable gate array (FPGA) and test on the proposed CBFF-SSD and VGG16 models. Experimental results show that our processor are more power efficient than general purpose central processing units (CPUs) and graphics processing units (GPUs), and have better performance density than other state-of-the-art FPGA-based designs. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking
Remote Sens. 2019, 11(17), 1967; https://doi.org/10.3390/rs11171967 - 21 Aug 2019
Cited by 5 | Viewed by 1063
Abstract
Thermal infrared (TIR) target tracking is a challenging task as it entails learning an effective model to identify the target in the situation of poor target visibility and clutter background. The sparse representation, as a typical appearance modeling approach, has been successfully exploited [...] Read more.
Thermal infrared (TIR) target tracking is a challenging task as it entails learning an effective model to identify the target in the situation of poor target visibility and clutter background. The sparse representation, as a typical appearance modeling approach, has been successfully exploited in the TIR target tracking. However, the discriminative information of the target and its surrounding background is usually neglected in the sparse coding process. To address this issue, we propose a mask sparse representation (MaskSR) model, which combines sparse coding together with high-level semantic features for TIR target tracking. We first obtain the pixel-wise labeling results of the target and its surrounding background in the last frame, and then use such results to train target-specific deep networks using a supervised manner. According to the output features of the deep networks, the high-level pixel-wise discriminative map of the target area is obtained. We introduce the binarized discriminative map as a mask template to the sparse representation and develop a novel algorithm to collaboratively represent the reliable target part and unreliable target part partitioned with the mask template, which explicitly indicates different discriminant capabilities by label 1 and 0. The proposed MaskSR model controls the superiority of the reliable target part in the reconstruction process via a weighted scheme. We solve this multi-parameter constrained problem by a customized alternating direction method of multipliers (ADMM) method. This model is applied to achieve TIR target tracking in the particle filter framework. To improve the sampling effectiveness and decrease the computation cost at the same time, a discriminative particle selection strategy based on kernelized correlation filter is proposed to replace the previous random sampling for searching useful candidates. Our proposed tracking method was tested on the VOT-TIR2016 benchmark. The experiment results show that the proposed method has a significant superiority compared with various state-of-the-art methods in TIR target tracking. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery
Remote Sens. 2019, 11(14), 1708; https://doi.org/10.3390/rs11141708 - 19 Jul 2019
Cited by 4 | Viewed by 1158
Abstract
Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, [...] Read more.
Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Unsupervised Saliency Model with Color Markov Chain for Oil Tank Detection
Remote Sens. 2019, 11(9), 1089; https://doi.org/10.3390/rs11091089 - 07 May 2019
Cited by 7 | Viewed by 1229
Abstract
Traditional oil tank detection methods often use geometric shape information. However, it is difficult to guarantee accurate detection under a variety of disturbance factors, especially various colors, scale differences, and the shadows caused by view angle and illumination. Therefore, we propose an unsupervised [...] Read more.
Traditional oil tank detection methods often use geometric shape information. However, it is difficult to guarantee accurate detection under a variety of disturbance factors, especially various colors, scale differences, and the shadows caused by view angle and illumination. Therefore, we propose an unsupervised saliency model with Color Markov Chain (US-CMC) to deal with oil tank detection. To avoid the influence of shadows, we make use of the CIE Lab space to construct a Color Markov Chain and generate a bottom-up latent saliency map. Moreover, we build a circular feature map based on a radial symmetric circle, which makes true targets to be strengthened for a subjective detection task. Besides, we combine the latent saliency map with the circular feature map, which can effectively suppress other salient regions except for oil tanks. Extensive experimental results demonstrate that it outperforms 15 saliency models for remote sensing images (RSIs). Compared with conventional oil tank detection methods, US-CMC has achieved better results and is also more robust for view angle, shadow, and shape similarity problems. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network
Remote Sens. 2019, 11(7), 755; https://doi.org/10.3390/rs11070755 - 28 Mar 2019
Cited by 26 | Viewed by 2271
Abstract
Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accurately classify and [...] Read more.
Object detection on very-high-resolution (VHR) remote sensing imagery has attracted a lot of attention in the field of image automatic interpretation. Region-based convolutional neural networks (CNNs) have been vastly promoted in this domain, which first generate candidate regions and then accurately classify and locate the objects existing in these regions. However, the overlarge images, the complex image backgrounds and the uneven size and quantity distribution of training samples make the detection tasks more challenging, especially for small and dense objects. To solve these problems, an effective region-based VHR remote sensing imagery object detection framework named Double Multi-scale Feature Pyramid Network (DM-FPN) was proposed in this paper, which utilizes inherent multi-scale pyramidal features and combines the strong-semantic, low-resolution features and the weak-semantic, high-resolution features simultaneously. DM-FPN consists of a multi-scale region proposal network and a multi-scale object detection network, these two modules share convolutional layers and can be trained end-to-end. We proposed several multi-scale training strategies to increase the diversity of training data and overcome the size restrictions of the input images. We also proposed multi-scale inference and adaptive categorical non-maximum suppression (ACNMS) strategies to promote detection performance, especially for small and dense objects. Extensive experiments and comprehensive evaluations on large-scale DOTA dataset demonstrate the effectiveness of the proposed framework, which achieves mean average precision (mAP) value of 0.7927 on validation dataset and the best mAP value of 0.793 on testing dataset. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
A Novel Multi-Model Decision Fusion Network for Object Detection in Remote Sensing Images
Remote Sens. 2019, 11(7), 737; https://doi.org/10.3390/rs11070737 - 27 Mar 2019
Cited by 17 | Viewed by 1729
Abstract
Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial structure information are still the existing problems. In [...] Read more.
Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial structure information are still the existing problems. In this paper, we propose a novel multi-model decision fusion framework which takes contextual information and multi-region features into account for addressing those problems. First, a contextual information fusion sub-network is designed to fuse both local contextual features and object-object relationship contextual features so as to deal with the problem of the diversity and complexity of geospatial object appearance. Second, a part-based multi-region fusion sub-network is constructed to merge multiple parts of an object for obtaining more spatial structure information about the object, which helps to handle the problem of the insufficient understanding of geospatial object spatial structure information. Finally, a decision fusion is made on all sub-networks to improve the stability and robustness of the model and achieve better detection performance. The experimental results on a publicly available ten class data set show that the proposed method is effective for geospatial object detection. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint
Remote Sens. 2019, 11(5), 559; https://doi.org/10.3390/rs11050559 - 07 Mar 2019
Cited by 24 | Viewed by 1732
Abstract
The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with [...] Read more.
The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm
Remote Sens. 2019, 11(4), 382; https://doi.org/10.3390/rs11040382 - 13 Feb 2019
Cited by 32 | Viewed by 1893
Abstract
Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and [...] Read more.
Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessArticle
Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting
Remote Sens. 2019, 11(2), 192; https://doi.org/10.3390/rs11020192 - 19 Jan 2019
Cited by 10 | Viewed by 1539
Abstract
Anomaly detection (AD), which aims to distinguish targets with significant spectral differences from the background, has become an important topic in hyperspectral imagery (HSI) processing. In this paper, a novel anomaly detection algorithm via dictionary construction-based low-rank representation (LRR) and adaptive weighting is [...] Read more.
Anomaly detection (AD), which aims to distinguish targets with significant spectral differences from the background, has become an important topic in hyperspectral imagery (HSI) processing. In this paper, a novel anomaly detection algorithm via dictionary construction-based low-rank representation (LRR) and adaptive weighting is proposed. This algorithm has three main advantages. First, based on the consistency with AD problem, the LRR is employed to mine the lowest-rank representation of hyperspectral data by imposing a low-rank constraint on the representation coefficients. Sparse component contains most of the anomaly information and can be used for anomaly detection. Second, to better separate the sparse anomalies from the background component, a background dictionary construction strategy based on the usage frequency of the dictionary atoms for HSI reconstruction is proposed. The constructed dictionary excludes possible anomalies and contains all background categories, thus spanning a more reasonable background space. Finally, to further enhance the response difference between the background pixels and anomalies, the response output obtained by LRR is multiplied by an adaptive weighting matrix. Therefore, the anomaly pixels are more easily distinguished from the background. Experiments on synthetic and real-world hyperspectral datasets demonstrate the superiority of our proposed method over other AD detectors. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessLetter
Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos
Remote Sens. 2019, 11(20), 2372; https://doi.org/10.3390/rs11202372 - 12 Oct 2019
Cited by 3 | Viewed by 1037
Abstract
Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are [...] Read more.
Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive samples only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Open AccessTechnical Note
Flood Distance Algorithms and Fault Hidden Danger Recognition for Transmission Line Towers Based on SAR Images
Remote Sens. 2019, 11(14), 1642; https://doi.org/10.3390/rs11141642 - 10 Jul 2019
Cited by 1 | Viewed by 917
Abstract
Synthetic Aperture Radar (SAR) has been extensively used in the monitoring of natural hazards such as floods and landslides. Predicting whether natural hazards will cause serious harm to important facilities on the ground is an important subject of study. In this study, the [...] Read more.
Synthetic Aperture Radar (SAR) has been extensively used in the monitoring of natural hazards such as floods and landslides. Predicting whether natural hazards will cause serious harm to important facilities on the ground is an important subject of study. In this study, the distance between the water body and the tower and the flood ratio in the search area and the elevation are defined as the evaluation indicators of the flood hazard of the tower, indicating whether flooding will threaten the safety of the transmission line tower. Herein, transmission tower flood identification algorithms based on the center distance of the tower and the grid distance of the tower are proposed. SAR satellite image data of the flood with a resolution of 10 m are selected to prove the feasibility and effectiveness of the proposed fault identification algorithm. The simulation results show that the SAR satellite image data with a resolution of 10 m can identify the distance accuracy of the transmission tower flood hazard by up to 7 m, which can be used to identify the flood fault of the transmission line tower. Full article
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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