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A UAV Video Dataset for Mosaicking and change detection From Low-Altitude Flights

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第6期50卷 2139-2149页

作者： Avola, Danilo Cinque, Luigi Foresti, Gian Luca Martinel, Niki Pannone, Daniele Piciarelli, Claudio Univ Udine Dept Math Comp Sci & Phys I-33100 Udine Italy Sapienza Univ Dept Comp Sci I-00185 Rome Italy

In recent years, the technology of small-scale unmanned aerial vehicles (UAVs) has steadily improved in terms of flight time, automatic control, and image acquisition. This has lead to the development of several applications for low-altitude tasks, such as vehicle tracking, person identification, and object recognition. These applications often require to stitch together several video frames to get a comprehensive view of large areas (mosaicking), or to detect differences between images or mosaics acquired at different times (change detection). However, the datasets used to test mosaicking and change detection algorithms are typically acquired at high-altitudes, thus ignoring the specific challenges of low-altitude scenarios. The purpose of this paper is to fill this gap by providing the UAV mosaicking and change detection dataset. It consists of 50 challenging aerial video sequences acquired at low-altitude in different environments with and without the presence of vehicles, persons, and objects, plus metadata and telemetry. In addition, this paper provides some performance metrics to evaluate both the quality of the obtained mosaics and the correctness of the detected changes. Finally, the results achieved by two baseline algorithms, one for mosaicking and one for detection, are presented. The aim is to provide a shared performance reference that can be used for comparison with future algorithms that will be tested on the dataset.

关键词： Video sequences change detection algorithms Cameras detection algorithms Task analysis Telemetry Unmanned aerial vehicles change detection dataset low-altitude flights mosaicking unmanned aerial vehicle (UAV)

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change detection in VHR Imagery With Severe Co-Registration Errors Using Deep Learning: A Comparative Study

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IEEE ACCESS 2022年 10卷 33723-33741页

作者： Kristollari, Viktoria Karathanassi, Vassilia Natl Tech Univ Athens Lab Remote Sensing Sch Rural Surveying & Geoinformat Engn Zografos 15780 Greece

change detection (CD) through Earth observation techniques can offer very significant information for monitoring tasks in a time-efficient manner. Very high-resolution (VHR) images can display objects in fine detail, thus making it possible to rapidly perceive isolated changes. However, this is a challenging task because of the increased within-class variance and geometric registration errors caused by different satellite view directions and angles. Lately, deep learning (DL) CD methods have proven very appealing for the CD problem because of their flexibility to combine and process different types of information along with the increased availability of higher processing power systems. Even though previous research has developed several notable DL methodologies, it has mostly focused on images with minor co-registration errors. Based on that, the goal of this study is to evaluate the performance of five state-of-the-art DL CD methods, two unsupervised and three supervised, on VHR images with severe co-registration errors. The methods are implemented on four urban European areas of versatile morphology. In addition, before applying the CD process, four popular automatic co-registration methods were evaluated because of the importance of this pre-processing step for the successful output of the CD problem. It was shown that phase correlation used on the Fourier-Mellin Transform produced the most satisfactory co-registration results and STANet detected building-related changes most successfully. Its success can be attributed to its particular attention mechanism and its training dataset. The rest of the co-registration and CD methods showed low performance.

关键词： Buildings Feature extraction Semantics Training Task analysis Satellites Principal component analysis change detection algorithms artificial neural networks very high-resolution imagery image registration land cover monitoring buildings

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GeoCDX: An Automated change detection and Exploitation System for High-Resolution Satellite Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2013年第4期51卷 2067-2086页

作者： Klaric, Matthew N. Claywell, Brian C. Scott, Grant J. Hudson, Nicholas J. Sjahputera, Ozy Li, Yonghong Barratt, Seth T. Keller, James M. Davis, Curt H. Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA Univ Missouri Ctr Geospatial Intelligence Columbia MO 65211 USA

We have developed a fully automated system for change detection of high-resolution satellite imagery. Our system, GeoCDX, is sensor-agnostic, resolution-independent and designed to process the very large volumes of data collected by modern high resolution panchromatic and multispectral imaging satellites. GeoCDX performs fully automated coregistration of imagery;extracts high-level features from the satellite imagery;performs change detection processing to pinpoint locations of change;clusters image tiles to group similar regions of change;and presents results in a variety of ways in an easy-to-use web application that facilitates online discovery, analysis, and dissemination of the change detection results. We applied GeoCDX to 4121 image pairs and successfully coregistered over 91% of the pairs covering a total area greater than 370 000 km(2);GeoCDX decreased the average coregistration error from 9.6 +/- 8.6 m to 1.8 +/- 1.2 m. We show that for some pairs, GeoCDX provides up to a 50% increase in users' efficiency compared to manually performing change detection in common GIS software. Moreover, the change detection assessment performed using GeoCDX was on average four times more accurate compared to the manual approach in large part due to the use of our change intensity map that provides visual cues to the user during exploitation. Finally, change detection analysis using GeoCDX resulted in a missed detection rate of less than 2%.

关键词： change detection algorithms feature extraction image registration remote sensing subtraction techniques

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A change-detection-Based Thompson Sampling Framework for Non-Stationary Bandits

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IEEE TRANSACTIONS ON COMPUTERS 2021年第10期70卷 1670-1676页

作者： Ghatak, Gourab IIIT Delhi Dept ECE Delhi 110020 India

We consider a non-stationary two-armed bandit framework and propose a change-detection-based Thompson sampling (TS) algorithm, named TS with change-detection (TS-CD), to keep track of the dynamic environment. The non-stationarity is modeled using a Poisson arrival process, which changes the mean of the rewards on each arrival. The proposed strategy compares the empirical mean of the recent rewards of an arm with the estimate of the mean of the rewards from its history. It detects a change when the empirical mean deviates from the mean estimate by a value larger than a threshold. Then, we characterize the lower bound on the duration of the time-window for which the bandit framework must remain stationary for TS-CD to successfully detect a change when it occurs. Consequently, our results highlight an upper bound on the parameter for the Poisson arrival process, for which the TS-CD achieves asymptotic regret optimality with high probability. Finally, we validate the efficacy of TS-CD by testing it for edge-control of radio access technique (RAT)-selection in a wireless network. Our results show that TS-CD not only outperforms the classical max-power RAT selection strategy but also other actively adaptive and passively adaptive bandit algorithms that are designed for non-stationary environments.

关键词： change detection algorithms Heuristic algorithms Upper bound Wireless networks Rats Microsoft Windows Bayes methods Multi-armed bandits thompson sampling non-stationary bandits millimeter-wave communication 5G

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A Forecasting Approach to Online change detection in Land Cover Time Series

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2019年第5期12卷 1451-1460页

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

We present a method for online detection of land cover change based on remotely sensed time series. change is detected by monitoring deviations between observations and forecasts made using the time series historical data and similar time series in the geographical region. This method and several others were applied to MODIS 8-day surface reflectance data for problems of detecting settlement expansion in Limpopo Province, South Africa, and detecting deforestation in New South Wales, Australia. The proposed method had significantly shorter median detection delay (DD) for equivalent rates of false alarms compared with the other evaluated methods. We obtained a median DD of seven samples for settlement detection and 14 samples for deforestation detection corresponding to 56 days and 112 days, respectively. This is compared with a median DD of 224 and 544 days for the best other methods evaluated. We suggest that the proposed method is an excellent candidate for land cover change detection where rapid detection is essential.

关键词： change detection algorithms remote sensing sequential detection time series analysis

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Enabling Reliable change detection for Independently Compressed SAR Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第8期55卷 4785-4794页

作者： McGuinness, Christopher Balster, Eric Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

This paper develops distortion metrics for compressed synthetic aperture radar (SAR) imagery from change detection test statistics. These metrics are used to predict lossy image compression's impact on change detection performance. The metrics do not require the intended change detection comparison image to provide these benefits. An SAR compression system leveraging the distortion metrics is proposed. The system generates a bad-pixel mask highlighting potential false alarms that are generated due to compression and are subsequently discarded in the change detection process. The proposed system's performance is demonstrated through noncoherent change detection analysis after JPEG2000 and JPEG image compression. Similarly, a coherent change detection system is evaluated after JPEG2000 image compression. For noncoherent change detection at large compression ratios (CRs) using JPEG2000, the proposed system provides a 33% reduction in false alarms at a 0.1 probability of detection as well as the ability to maintain near-distortionless false alarm rates across a wide range of CRs. At a 0.1 probability of detection for coherent change detection, the system provides a 37% reduction in false alarms at modest CRs. The coherent change detection system is also demonstrated to maintain low false alarm rates across a range of CRs.

关键词： change detection algorithms compression algorithms synthetic aperture radar (SAR)

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A Siamese Network Based U-Net for change detection in High Resolution Remote Sensing Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 2357-2369页

作者： Chen, Tao Lu, Zhiyuan Yang, Yue Zhang, Yuxiang Du, Bo Plaza, Antonio China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Peoples R China China Acad Railway Sci Corp Ltd Inst Comp Technol Beijing 100081 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430079 Peoples R China Univ Extremadura Escuela Politecn Dept Technol Comp & Commun Hyperspectral Comp Lab Caceres 10071 Spain

Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area. Traditional RSICD algorithms exhibit poor performance on complex change detection (CD) tasks. In recent years, deep learning (DL) techniques have achieved outstanding results in the fields of RSI segmentation and target recognition. In CD research, most of the methods treat multitemporal remote sensing data as one input and directly apply DL-based image segmentation theory on it while ignoring the spatio-temporal information in these images. In this article, a new siamese neural network is designed by combing an attention mechanism (Siamese_AUNet) with UNet to solve the problems of RSICD algorithms. SiameseNet encodes the feature extraction of RSIs by two branches in the siamese network, respectively. The weights are shared between these two branches in siamese networks. Subsequently, an attention mechanism is added to the model in order to improve its detection ability for changed objects. The models are then compared with conventional neural networks using three benchmark datasets. The results show that the Siamese_AUNet newly proposed in this article exhibits better performance than other standard methods when solving problems related to weak CD and noise suppression.

关键词： Feature extraction Object oriented modeling Biological system modeling Sensors Image segmentation change detection algorithms Electronic mail Attention blocks change detection (CD) remote sensing siamese networks

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Land Cover change detection Using the Internal Covariance Matrix of the Extended Kalman Filter Over Multiple Spectral Bands

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2013年第3期6卷 1079-1085页

作者： Salmon, Brian Paxton Kleynhans, Waldo van den Bergh, Frans Olivier, Jan Corne Grobler, Trienko Lups Wessels, Konrad Johan CSIR Remote Sensing Res Unit Meraka Inst ZA-0001 Pretoria South Africa Univ Pretoria Dept Elect Elect & Comp Engn ZA-0001 Pretoria South Africa Univ Tasmania Sch Engn Hobart Tas 7005 Australia CSIR Def Peace Safety & Secur Unit ZA-0001 Pretoria South Africa

In this paper, the internal operations of an Extended Kalman Filter is investigated to observe if information can be derived to detect land cover change in a MODerate-resolution Imaging Spectroradiometer (MODIS) time series. The concept is based on the internal covariance matrix used by the Extended Kalman Filter, which adjusts the internal state of the filter for any changes occurring in the time series. The Extended Kalman Filter expands the internal covariance matrix if a significant change in reflectance value is observed, followed by adapting the state parameters to compensate for this change. The analysis shows that a change detection accuracy above 90% can be attained when evaluating the elements within the internal covariance matrix to detect new human settlements, with a corresponding false alarm rate below 6%.

关键词： change detection algorithms covariance matrix Kalman filter spatial information time series analysis

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SAR Image change detection Based on Joint Dictionary Learning With Iterative Adaptive Threshold Optimization

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 5234-5249页

作者： Yu, Qiuze Zhang, Miao Yu, Lijie Wang, Ruikai Xiao, Jinsheng Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China

Synthetic aperture radar (SAR) image change detection is still a challenge due to inherent speckle noise and scarce datasets. This article proposes a joint-related dictionary learning algorithm based on the k-singular value decomposition (K-SVD) algorithm called JR-KSVD and an iterative adaptive threshold optimization (IATO) algorithm for unsupervised change detection. The JR-KSVD algorithm adds dictionary correlation learning to the K-SVD algorithm to generate a uniform initial dictionary for dual-temporal SAR images, thereby reducing the instability of sparse representations due to atomic correlations and enhancing the extraction of image edges and details. The IATO approach employs thresholds obtained by the "difference-log ratio" fusion image for indefinite residual energy minimization iterations to gradually shrink the threshold variation range and finally generate the change images, which have a high degree of adaptivity and strong real-time performance. Finally, experiments on six real datasets demonstrate that the proposed algorithm exhibits superior detection performance and robustness against some state-of-the-art algorithms.

关键词： Radar polarimetry Dictionaries Machine learning Image reconstruction change detection algorithms Synthetic aperture radar Matching pursuit algorithms change detection difference-log ratio image iterative adaptive threshold joint-related dictionary learning synthetic aperture radar (SAR) image

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Increasing Robustness of Postclassification change detection Using Time Series of Land Cover Maps

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2012年第9期50卷 3327-3339页

作者： Kempeneers, Pieter Sedano, Fernando Strobl, Peter McInerney, Daniel O. San-Miguel-Ayanz, Jesus Flemish Inst Technol Res VITO Ctr Remote Sensing & Earth Observat Proc TAP B-2400 Mol Belgium Commiss European Communities Joint Res Ctr IES LMNH FOREST TP 261 I-21027 Ispra VA Italy

The monitoring of land cover requires that stable land cover classes be distinguished from changes over time. Within this paper, a postclassification method is presented that provides land cover change information, based on a time series of land cover maps. The method applies a kernel filter to sequential land cover maps. Under some basic assumptions, it shows robustness against classification errors. Despite seasonality, land cover changes often occur at a low temporal frequency (e. g., maximum once every 5-10 years). If land cover maps are available more frequently, some of the information will become redundant (oversampling). The proposed method uses this redundancy for tolerating (nonsystematic) misclassifications. In order to demonstrate the benefits and limitations of the proposed method, analytical expressions have been derived. When compared to a simple postclassification comparison, one of the key strengths of the proposed approach is that it is able to improve both the overall and user's accuracy of change, while also maintaining the same level of producer's accuracy. As a case study, MODerate Resolution Imaging Spectroradiometer remote sensing data from 2006-2010 were classified into forest (F)/nonforest (NF) at pan-European scale. Promising results were obtained for detecting forest loss due to natural disasters. Quality was assessed using burnt area maps in southern Europe and a forest damage report after a windstorm in France. Results indicated a considerable reduction of change detection errors, confirming the theoretical results.

关键词： change detection algorithms forestry image classification

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