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Deep Atrous Spatial Features-Based Supervised Foreground detection Algorithm for Industrial Surveillance Systems

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第7期17卷 4818-4826页

作者： Shahbaz, Ajmal Jo, Kang-Hyun Univ Ulsan Sch Elect & Comp Engn Ulsan 44610 South Korea Univ Ulsan Dept Elect & Comp Engn Ulsan 44610 South Korea

Camera-based surveillance systems largely perform an intrusion detection task for sensitive areas. The task may seem trivial but is quite challenging due to environmental changes and object behaviors such as those due to night-time, sunlight, IR camera, camouflage, and static foreground objects, etc. Convolutional neural network based algorithms have shown promise in dealing with these challenges. However, they are exclusively focused on accuracy. This article proposes an efficient supervised foreground detection (SFDNet) algorithm based on atrous deep spatial features. The features are extracted using atrous convolution kernels to enlarge the field-of-view of a kernel mask, thereby encoding rich context features without increasing the number of parameters. The network further benefits from a residual dense block strategy that mixes the mid and high-level features to retain the foreground information lost in low-resolution high-level features. The extracted features are expanded using a novel pyramid upsampling network. The feature maps are upsampled using bilinear interpolation and pass through a 3x3 convolutional kernel. The expanded feature maps are concatenated with the corresponding mid and low-level feature maps from an atrous feature extractor to further refine the expanded feature maps. The SFDNet showed better performance than high-ranked foreground detection algorithms on the three standard databases. The testing demo can be found at https://***/file/d/1z_zEj9Yp7GZeM2gSIwYKvSzQlxMAiarw/view?usp=sharing.

关键词： Feature extraction Convolution Kernel Surveillance Standards detection algorithms change detection algorithms Convolutional neural networks (CNN) dual-camera sensors infrared (IR) camera intelligent surveillance systems

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change detection with 1 m resolution satellite and aerial images

Change detection with 1 m resolution satellite and aerial im...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： H. Spitzer R. Franck M. Kollewe N. Rega A. Rothkirch R. Wiemker II. Institut für Experimentalphysik Universit#x00E4 t Hamburg Hamburg Germany

We propose an optimization of a computer based change detection technique based on Iterative Principal Component Analysis (IPCA). We determine and evaluate the changes between an airborne and a spaceborne multispectral image data set, the latter recorded by the commercial satellite IKONOS-2. The change detection algorithm proved to be applicable to large remotely sensed data sets. A vegetation filter, a shadow filter and an oversaturation filter improved the accuracy of the results. When applying all filters more than 80 percent of the objects with changes due to construction activity are detected by the IPCA algorithm. The false alarm rate (change of an object indicated but not verified) is about 5 percent.

关键词： Image resolution Satellites Principal component analysis Vegetation mapping Iterative algorithms Chromium Multispectral imaging Optical filters change detection algorithms Optical scattering

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Forest Disaster detection Method Based on Ensemble Spatial-Spectral Genetic Algorithm

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 7375-7390页

作者： Cao, Yang Feng, Wei Quan, Yinghui Bao, Wenxing Dauphin, Gabriel Ren, Aifeng Yuan, Xiaoguang Xing, Mengdao Xidian Univ Sch Elect Engn Dept Remote Sensing Sci & Technol Xian 710071 Peoples R China North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Ningxia Peoples R China Univ Paris XIII Inst Galilee Lab Informat Proc & Transmiss F-93430 Paris France Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Peoples R China

Remote sensing image change detection is the key technology for monitoring forest windfall damages. A genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection in forest areas. However, traditional GAs remain challenging due to several issues, such as complex calculation, poor noise immunity, and slow convergence. Analysis at the spatial level allows classifications to utilize the contextual and hierarchical information of image objects in addition to solely using spectral information. In addition, ensemble learning presents a possibility for improving classification accuracy. Ensemble classifiers combined with the spatial-based GA offers a promising method [ensemble spatial-spectral genetic algorithm (E-nGA)] for automating the process of monitoring forest loss. The research in this article is presented in four parts. First, block-matching and 3-D filtering is performed to suppress noises while enhancing valuable information. The difference image is, then, generated using the image difference method. Afterward, context-based saliency detection and fuzzy c-means algorithm are conducted on the difference image to reduce the search space. Finally, the proposed E-nGA is executed to further classify the pixels and produce the final change map. Our first proposition is to design improved genetic operators in the GA, relying not only on pixel values but also on spatial information. Our second proposition is to consider an ensemble classification model based on multiple vegetation features for decision integration. Six frequently used classification methods, as well as the simple GA, are executed to demonstrate the effectiveness of the proposed framework in improving the robustness and detection accuracy.

关键词： Forestry Genetic algorithms Storms Vegetation mapping Linear programming change detection algorithms Remote sensing Decision ensemble forest disaster detection genetic algorithm (GA) locality window multispectral vegetation features

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change detection Method of High Resolution Remote Sensing Image Based on UNet Network

Change Detection Method of High Resolution Remote Sensing Im...

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Intelligent Computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Zihao Liu Yu Zhu Chengkun Zhang Qinghai Provincial Laboratory for Intelligent Computing and Application Qinghai University Xining Qinghai China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

To address the issues of inadequate feature extraction, complicated models, and a large number of network parameters, this paper proposes a change detection method for high-resolution remote sensing images based on UNet network. Firstly, Resnet50 network is selected in the encoder part to obtain deeper features, avoid gradient disappearance and reduce training time. The scSE attention module structure is introduced in the skip connection. This module enhances the network's ability to prioritize the important channels and mine the local information better. Then, in order to improve the segmentation accuracy and obtain the context information better, atrous spatial pyramid pool is carried out in the deepest feature layer of the encoder to enrich the representation of information and obtain the multi-scale information of the target. Finally, the combination of Focal loss function and BCE loss weight was presented with improving model learning strategy. On the LEVIR-CD and CDD datasets, it is shown that proposed method has improved precision rate, recall rate and F1, and the number of model parameters is 5.37M, which is superior to other commonly used change detection networks.

关键词： Training Human computer interaction Accuracy Image resolution change detection algorithms Feature extraction Sensors Remote sensing Residual neural networks Context modeling

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A Unified Framework for Multimodal Submodular Integrated Circuits Trojan detection

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2011年第1期6卷 162-174页

作者： Koushanfar, Farinaz Mirhoseini, Azalia Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

This paper presents a unified formal framework for integrated circuits (ICs) Trojan detection that can simultaneously employ multiple noninvasive side-channel measurement types (modalities). After formally defining the IC Trojan detection for each side-channel measurement and analyzing the complexity, we devise a new submodular formulation of the problem objective function. Based on the objective function properties, an efficient Trojan detection method with strong approximation and optimality guarantees is introduced. Signal processing methods for calibrating the impact of interchip and intrachip correlations are presented. We define a new sensitivity metric that formally quantifies the impact of modifications to each existing gate that is affected by Trojan. Using the new metric, we compare the Trojan detection capability of different measurement types for static (quiescent) current, dynamic (transient) current, and timing (delay) side-channel measurements. We propose four methods for combining the detection results that are gained from different measurement modalities and show how the sensitivity results can be used for a systematic combining of the detection results. Experimental evaluations on benchmark designs reveal the low-overhead and effectiveness of the new Trojan detection framework and provides a comparison of different detection combining methods.

关键词： change detection algorithms circuit Trojan detection gate-level characterization hardware malware detection hardware security and trust submodular functions timing/power tests

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Unsupervised Land Cover change Estimation Using Region Covariance Estimates

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2019年第3期16卷 347-351页

作者： Olding, W. C. Olivier, J. C. Salmon, B. P. Kleynhans, W. Univ Tasmania Sch Engn Hobart Tas 7005 Australia CSIR Meraka Inst Remote Sensing Res Unit ZA-0081 Pretoria South Africa

In this letter, we demonstrate the utility of estimating a probabilistic model of the underlying seasonal and interannual variations experienced by land cover time series in a given geographical region. Time series that deviate from these trajectories due to the human-induced change appear as outliers and can be detected using their Mahalanobis distance from the mean under the joint distribution of time samples. We apply this model to a collection of pixel time series acquired by the Moderate Resolution Imaging Spectroradiometer platform over Limpopo province, South Africa, for the task of identifying human settlement expansion. For estimation of the time of change, we present a hypothesis testing approach that tests for a decrease in correlation between samples before and after the change. This was found to be highly effective, yielding a mean absolute error of 52 days.

关键词： change detection algorithms covariance matrices density estimation robust algorithm remote sensing time series analysis

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Adaptive Filtering With Reduced Computational Complexity Using SOPOT Arithmetic

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第2期69卷 746-756页

作者： da Silveira Coelho, Luiz Felipe Lovisolo, Lisandro Tcheou, Michel Pompeu Univ Fed Rio de Janeiro COPPE UFRJElect Engn Program PEE Inst Alberto Luiz Coimbra Posgrad & Pesquisa Engn BR-21941972 Rio De Janeiro Brazil Univ Estado Rio de Janeiro UERJ Dept Elect & Telecommun Engn DETEL BR-21945970 Rio De Janeiro Brazil

Implementing finite impulse response (FIR) adaptive filters by employing the sums of signed-powers-of-two (SOPOT) arithmetic may lead to simpler hardware and consequently reduced power consumption. In this paper, one evaluates the effects of SOPOT arithmetic on the adaptive filter's recursion algorithms. The filters' coefficients and algorithms' underlying variables are fully operated using SOPOT arithmetic in the whole iterative process. More specifically, one evaluates convergence rate, numerical stability, and accuracy since using few signed-powers-of-two (SPT) terms propagates numerical errors during the adaptive cycle that may impair the algorithm behavior. The SOPOT approximations are obtained through the technique known as Matching Pursuits with Generalized Bit-Plane (MPGBP) algorithm, with notable cost-performance trade-off and low computational complexity. Results are provided for the Least-Mean-Squares (LMS), the Normalized Least-Mean-Squares (NLMS) and the Recursive-Least-Squares (RLS) algorithms, considering adaptive filters employed for system identification and change detection.

关键词： Approximation algorithms Arithmetic Finite impulse response filters Convergence change detection algorithms Computational complexity Matching pursuit algorithms Sums of signed-powers-of-two adaptive filters SPT-LMS SPT-NLMS SPT-RLS

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A New Coherent Similarity Measure for Temporal Multichannel Scene Characterization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2012年第7期50卷 2839-2851页

作者： Erten, Esra Reigber, Andreas Ferro-Famil, Laurent Hellwich, Olaf Swiss Fed Inst Technol Chair Earth Observat & Remote Sensing Inst Environm Engn CH-8093 Zurich Switzerland German Aerosp Ctr DLR Microwaves & Radar Inst D-82230 Wessling Germany Univ Rennes 1 Inst Elect & Telecommun Rennes F-35042 Rennes France Tech Univ Berlin Dept Comp Vis & Remote Sensing D-10587 Berlin Germany

This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolInSAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection.

关键词： change detection algorithms mutual information parameter estimation polarimetric synthetic aperture radar

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A Method for Fault detection and Isolation Based on the Processing of Multiple Diagnostic Indices: Application to Inverter Faults in AC Drives

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2013年第3期62卷 995-1009页

作者： Meinguet, Fabien Sandulescu, Paul Kestelyn, Xavier Semail, Eric Arts & Metiers ParisTech Lab Elect Engn & Power Elect Lille L2EP F-59000 Lille France

A general method for fault detection and isolation (FDI) is proposed and applied to inverter faults in drives of electric vehicles (EVs). This method is based on a change detection algorithm, which allows multiple fault indices (FIs) to be combined to retrieve the most likely state of the drive. The drive topology under study is a six-leg inverter associated with a three-phase open-end winding machine. Due to the inherent fault-tolerant topology, the conventional FIs are no longer effective. Therefore, an analysis of simulations under faulty conditions leads to the derivation of suitable FIs. These are based on the envelope of the phase currents, as well as their instantaneous frequency. Specific operating conditions related to the EV environment are taken into account, such as the flux-weakening region and energy recovery. In these modes of operation, FDI can be affected by uncontrolled currents circulating through the free-wheeling diodes. Finally, the performances of the FDI scheme are evaluated under steady-state and nonstationary conditions through simulations and experimental results.

关键词： AC machines change detection algorithms condition monitoring drives electric vehicles (EVs) fault detection fault diagnosis inverters permanent-magnet machines phase locked loops

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UNSUPERVISED SEGMENTATION OF TEXTURED IMAGES BY EDGE-detection IN MULTIDIMENSIONAL FEATURES

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1989年第4期11卷 414-421页

作者： KHOTANZAD, A CHEN, JY CHUNG SHAN INST SCI & TECHNOL TAIPEI TAIWAN

An algorithm for unsupervised texture segmentation is developed that is based on detecting changes in textural characteristics of small local regions. Six features derived from two, two-dimensional, noncausal random field models are used to represent texture. These features contain information about gray-level-value variations in the eight principal directions. An algorithm for automatic selection of the size of the observation windows over which textural activity and change are measured has been developed. Effects of changes in individual features are considered simultaneously by constructing a one-dimensional measure of textural change from them. Edges in this measure correspond to the sought-after textural edges. Experiments results with images containing regions of natural texture show that the algorithm performs very well.

关键词： Image segmentation Image edge detection change detection algorithms Size measurement INSPEC: Controlled Indexing computerised picture processing computerised pattern recognition INSPEC: Non-Controlled Indexing observation windows 2D noncausal random field models computerised picture processing pattern recognition textured images edge detection multidimensional feature unsupervised texture segmentation gray-level-value

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