Artykuły w czasopismach: „Remote sensing - video”

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Ye, Fanghong, Tinghua Ai, Jiaming Wang, Yuan Yao i Zheng Zhou. "A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data". Remote Sensing 14, nr 10 (11.05.2022): 2324. http://dx.doi.org/10.3390/rs14102324.

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The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.

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MEISNER, D. "Fundamentals of airborne video remote sensing☆". Remote Sensing of Environment 19, nr 1 (luty 1986): 63–79. http://dx.doi.org/10.1016/0034-4257(86)90041-6.

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Su, Zhijuan, Gang Wan, Wenhua Zhang, Ningbo Guo, Yitian Wu, Jia Liu, Dianwei Cong, Yutong Jia i Zhanji Wei. "An Integrated Detection and Multi-Object Tracking Pipeline for Satellite Video Analysis of Maritime and Aerial Objects". Remote Sensing 16, nr 4 (19.02.2024): 724. http://dx.doi.org/10.3390/rs16040724.

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Optical remote sensing videos, as a new source of remote sensing data that has emerged in recent years, have significant potential in remote sensing applications, especially national defense. In this paper, a tracking pipeline named TDNet (tracking while detecting based on a neural network) is proposed for optical remote sensing videos based on a correlation filter and deep neural networks. The pipeline is used to simultaneously track ships and planes in videos. There are many target tracking methods for general video data, but they suffer some difficulties in remote sensing videos with low resolution and those influenced by weather conditions. The tracked targets are usually misty. Therefore, in TDNet, we propose a new multi-target tracking method called MT-KCF and a detecting-assisted tracking (i.e., DAT) module to improve tracking accuracy and precision. Meanwhile, we also design a new target recognition (i.e., NTR) module to recognise newly emerged targets. In order to verify the performance of TDNet, we compare our method with several state-of-the-art tracking methods on optical video remote sensing data sets acquired from the Jilin No. 1 satellite. The experimental results demonstrate the effectiveness and the state-of-the-art performance of the proposed method. The proposed method can achieve more than 90% performance in terms of precision for single-target tracking tasks and more than 85% performance in terms of MOTA for multi-object tracking tasks.

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Wang, Y., H. Cheng, X. Zhou, W. Luo i H. Zhang. "MOVING SHIP DETECTION AND MOVEMENT PREDICTION IN REMOTE SENSING VIDEOS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (14.08.2020): 1303–8. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1303-2020.

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Abstract. With the rapid development of remote sensing technology, it is possible to obtain continuous video data from outer space successfully. It is of great significance in military and civilian fields to detect moving objects from the remote sensing image sequence and predict their movements. In recent years, this issue has attracted more and more attention. However, researches on moving object detection and movement prediction in high-resolution remote sensing videos are still in its infancy, which is worthy of further study. In this paper, we propose a ship detection and movement prediction method based on You-Only-Look-Once (YOLO) v3 and Simple Online and Realtime Tracking (SORT). Original YOLO v3 is improved by multi-frame training to fully utilize the information of continuous frames in a fusion way. The simple and practical multiple object tracking algorithm SORT is used to recognize multiple targets detected by multi-frame YOLO v3 model and obtain their coordinates. These coordinates are fitted by the least square method to get the trajectories of multiple targets. We take the derivative of each trajectory to obtain the real-time movement direction and velocity of the detected ships. Experiments are performed on multi-spectral remote sensing images selected on Google Earth, as well as real multi-spectral remote sensing videos captured by Jilin-1 satellite. Experimental results validate the effectiveness of our method for moving ship detection and movement prediction. It shows a feasible way for efficient interpretation and information extraction of new remote sensing video data.

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Sun, Siqiu, i Tianbo Xiong. "Application of Remote Sensing Technology in Sustainable Urban Planning and Development". Applied and Computational Engineering 3, nr 1 (25.05.2023): 283–88. http://dx.doi.org/10.54254/2755-2721/3/20230475.

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Urban planning is super vital for cities. Good planning for the city would provide more convenience and benefits to citizens through economic effects, beautiful scenery and so on. This study analyses the application of remote sensing technologies in urban landscaping planning, transportation, and environmental protection. The combination of remote sensing and GIS technologies makes urban landscaping planning more accessible in urban landscaping planning. Usually, remote sensing provides satellite images, while the GIS processes the image and compares the target region for analysis. The combination method could predict urban green spaces. In urban transportation planning, remote sensing technology could combine with Artificial intelligence smart video technology to protect peoples safety in transportation, such as observing traffic violations by capturing videos or images and protecting traffic safety. In environmental protection, remote sensing could monitor the area change of wetlands and solve the natural disasters around the city. Different approaches have their benefits and disadvantages, and thus making urban plans by combining various techniques is essential.

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Baldock, Tom E., Theo Moura i Hannah E. Power. "Video-Based Remote Sensing of Surf Zone Conditions". IEEE Potentials 36, nr 2 (marzec 2017): 35–41. http://dx.doi.org/10.1109/mpot.2016.2631018.

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KANG, Jinzhong, Guizhou WANG, Guojin HE, Huihui WANG, Ranyu YIN, Wei JIANG i Zhaoming ZHANG. "Moving vehicle detection for remote sensing satellite video". National Remote Sensing Bulletin 24, nr 9 (2020): 1099–107. http://dx.doi.org/10.11834/jrs.20208364.

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Lei, Lei, i Dongen Guo. "Multitarget Detection and Tracking Method in Remote Sensing Satellite Video". Computational Intelligence and Neuroscience 2021 (31.08.2021): 1–7. http://dx.doi.org/10.1155/2021/7381909.

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A remote sensing video satellite multiple object detection and tracking method based on road masking, Gaussian mixture model (GMM), and data association is proposed. This method first extracts the road network from the remote sensing video based on deep learning. In the detection stage, the background subtraction algorithm is used based on the GMM to obtain the detection results of the moving targets on the road. In the tracking stage, the data association of the same target detection result in adjacent frames is realized based on the neighborhood search algorithm, so as to obtain the continuous tracking trajectory of each target. The experiments about multiobject detection and tracking are conducted on data measure by real remote sensing satellites, and the results verified the feasibility of the proposed method.

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Everitt, James H., David E. Escobar, Mario A. Alaniz, Ricardo Villarreal i Michael R. Davis. "Distinguishing Brush and Weeds on Rangelands Using Video Remote Sensing". Weed Technology 6, nr 4 (grudzień 1992): 913–21. http://dx.doi.org/10.1017/s0890037x00036472.

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This paper describes the application of a relatively new remote sensing tool, airborne video imagery, for distinguishing weed and brush species on rangelands. Plant species studied were false broomweed, spiny aster, and Chinese tamarisk. A multispectral video system that acquired color-infrared (CIR) composite imagery and its simultaneously synchronized three-band [near-infrared (NIR), red, and yellow-green] narrowband images was used for the false broomweed and spiny aster experiments. A conventional color camcorder video system was used to study Chinese tamarisk. False broomweed and spiny aster could be detected on CIR composite and NIR narrowband imagery, while Chinese tamarisk could be distinguished on conventional color imagery. Quantitative data obtained from digitized video images of the three species showed that their digital values were statistically different (P = 0.05) from those of associated vegetation and soil. Computer analyses of video images showed that populations of the three species could be quantified from associated vegetation. This technique permits area estimates of false broomweed, spiny aster, and Chinese tamarisk populations on rangeland and wildland areas.

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Wu, Yiguang, Meizhen Wang, Xuejun Liu, Ziran Wang, Tianwu Ma, Zhimin Lu, Dan Liu, Yujia Xie, Xiuquan Li i Xing Wang. "Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing". Remote Sensing 13, nr 19 (26.09.2021): 3853. http://dx.doi.org/10.3390/rs13193853.

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Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring.

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Shilin, B. V., i A. Y. Kuznetsov. "Place of video spectral imaging among remote sensing methods". Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 19, nr 1 (2022): 9–24. http://dx.doi.org/10.21046/2070-7401-2022-19-1-9-24.

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Everitt, J. H., D. E. Escobar, M. A. Alaniz i M. R. Davis. "Light Reflectance Characteristics and Video Remote Sensing of Pricklypear". Journal of Range Management 44, nr 6 (listopad 1991): 587. http://dx.doi.org/10.2307/4003041.

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Li, Sheng-liang, Kun Liu, Li Zhang, Jie Wang, Zhi-zhou Zhang i Da-peng Han. "Correlation estimation for remote sensing compressed-sensed video sampling". Journal of Electronic Imaging 23, nr 6 (18.11.2014): 063007. http://dx.doi.org/10.1117/1.jei.23.6.063007.

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Ma, Yong. "Research on Intelligent Evaluation System of Sports Training based on Video Image Acquisition and Scene Semantics". Advances in Multimedia 2022 (26.03.2022): 1–6. http://dx.doi.org/10.1155/2022/4726450.

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This paper proposes to check the travel target of the dynamic background in the video surveillance with a fixed camera. A travel target detection method based on video picture acquisition and scene semantics for surveillance video was proposed. First, on the basis of combing the concepts and methods of picture recognition, the semantic information of the scene was fused to eliminate the interference factors in the unnecessary detection area. Secondly, a remote sensing picture visual feature representation method containing a semantic recognition method of remote sensing picture scenes and CSIFT features based on PLSA was presented. 10 types of typical remote sensing picture scenes are used for tests, and the visual vocabulary extraction method remains the same. The fixed visual vocabulary was 600, and the potential semantic subjects changes between 8∼50. The test results indicated that the highest average recognition rate was obtained when the latent semantic topics were 20. Inappropriate latent semantic topics will lead to a decline in recognition rates. The effectiveness of this method was fully verified.

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Zhang, Rui, Xueyang Zhang, Longlong Xiao i Jiayu Qiu. "Recognition of Aircraft Activities at Airports on Video Micro-Satellites: Methodology and Experimental Validation". Aerospace 9, nr 8 (30.07.2022): 414. http://dx.doi.org/10.3390/aerospace9080414.

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The remote sensing satellite constellation based on micro-satellites is an important means to construct a global and all-sky earth observation system in the future. Therefore, realizing the recognition of aircraft activities on video micro-satellites is a key technology that needs to be solved urgently. In this paper, an efficient algorithm for aircraft activity recognition that can be deployed on video micro-satellites was proposed. First, aircraft detection was performed on the first incoming remote sensing image using a robust DCNN-based object detection model. Then, a multi-target tracking model incorporating geospatial information was built for aircraft tracking and activity recognition. The algorithm was deployed on an embedded AI computer which was a COTS component. The algorithm was verified using remote sensing videos from commercial micro-satellites. Experimental results show that the algorithm can process aircraft targets of different sizes, and is equally effective even with complex environmental backgrounds, lighting conditions, and various movements of the aircraft, such as turning, entering, and exiting. Based on aircraft tracking results and geospatial information, the motion speed of each aircraft can be obtained, and its activity can be divided into parking, taxiing, or flying. The scheme proposed in this paper has good application prospects in the realization of on-orbit event recognition in micro-satellites with limited computing and memory resources.

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Mei, Ling, Yizhuo He, Farnoosh Javadi Fishani, Yaowen Yu, Lijun Zhang i Helge Rhodin. "Learning Domain-Adaptive Landmark Detection-Based Self-Supervised Video Synchronization for Remote Sensing Panorama". Remote Sensing 15, nr 4 (9.02.2023): 953. http://dx.doi.org/10.3390/rs15040953.

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The synchronization of videos is an essential pre-processing step for multi-view reconstruction such as the image mosaic by UAV remote sensing; it is often solved with hardware solutions in motion capture studios. However, traditional synchronization setups rely on manual interventions or software solutions and only fit for a particular domain of motions. In this paper, we propose a self-supervised video synchronization algorithm that attains high accuracy in diverse scenarios without cumbersome manual intervention. At the core is a motion-based video synchronization algorithm that infers temporal offsets from the trajectories of moving objects in the videos. It is complemented by a self-supervised scene decomposition algorithm that detects common parts and their motion tracks in two or more videos, without requiring any manual positional supervision. We evaluate our approach on three different datasets, including the motion of humans, animals, and simulated objects, and use it to build the view panorama of the remote sensing field. All experiments demonstrate that the proposed location-based synchronization is more effective compared to the state-of-the-art methods, and our self-supervised inference approaches the accuracy of supervised solutions, while being much easier to adapt to a new target domain.

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Yan MA, Yan MA, Chi MA Chi MA, Yan-hao XIE Yan-hao XIE i Fang WANG Fang WANG. "Space Target Luminosity Measurement Based on Video Remote Sensing Satellites". ACTA PHOTONICA SINICA 48, nr 12 (2019): 1228002. http://dx.doi.org/10.3788/gzxb20194812.1228002.

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S. J. Thomson, P. V. Zimba, C. T. Bryson i V. J. Alarcon-Calderon. "POTENTIAL FOR REMOTE SENSING FROM AGRICULTURAL AIRCRAFT USING DIGITAL VIDEO". Applied Engineering in Agriculture 21, nr 3 (2005): 531–37. http://dx.doi.org/10.13031/2013.18445.

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Everitt, J. H., i P. R. Nixon. "Video Imagery: A New Remote Sensing Tool for Range Management". Journal of Range Management 38, nr 5 (wrzesień 1985): 421. http://dx.doi.org/10.2307/3899713.

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Kroon, A., M. A. Davidson, S. G. J. Aarninkhof, R. Archetti, C. Armaroli, M. Gonzalez, S. Medri i in. "Application of remote sensing video systems to coastline management problems". Coastal Engineering 54, nr 6-7 (czerwiec 2007): 493–505. http://dx.doi.org/10.1016/j.coastaleng.2007.01.004.

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Jensen, Hans V., Jørn H. S. Andersen, Per S. Daling i Elisabeth Nøst. "RECENT EXPERIENCE FROM MULTIPLE REMOTE SENSING AND MONITORING TO IMPROVE OIL SPILL RESPONSE OPERATIONS". International Oil Spill Conference Proceedings 2008, nr 1 (1.05.2008): 407–12. http://dx.doi.org/10.7901/2169-3358-2008-1-407.

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ABSTRACT Introducing regular aerial surveillance in 1981 and near-real time radar satellite detection services in 1992, Norway has obtained a substantial experience in multi sensor oil spill remote sensing. Since 2001 NOFO has been a driving force in the development and utilization of ship-based sensors for short to medium range oil spill detection, supplementing airborne and satellite remote sensing. During the NOFO Oil On Water Exercise in 2006 two satellites, four aircraft, one helicopter and two ships carrying wide range of sensors provided a unique opportunity to assess and compare remote sensing field data synchronized with ground-truth sampling from three sampling MOB-boats. The sampling boats were equipped for doing oil slick thickness measurements and physical-chemical characterization of the surface oil properties. A new vessel-based dispersant application system was field tested executing dispersant treatment of two oil slicks while supported by live infrared video transmitted to the vessel from helicopter. The success of this experiment was documented by extensive monitoring and characterization of the surface oil and the dispersed oil plume during and after the dispersant treatment. This guiding technique, in using aerial forward looking IR-video live transmission from helicopter and remote sensing aircraft, has been practiced later during a recent accidental oil spill on the Norwegian continental shelf. To utilize multiple remote sensors operationally from a response vessel, it is necessary to compare signatures from different sensors in near real time. This paper describes core elements of the remote sensing and ground-truth monitoring during oil on water exercises in recent years, lessons learned and how NOFO will continue developing remote sensing operations related to oil spill combating in reduced visibility and light conditions.

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Zhou, Jinjia, Ittetsu Taniguchi i Xin Jin. "Image/Video Coding and Processing Techniques for Intelligent Sensor Nodes". Sensors 24, nr 15 (25.07.2024): 4819. http://dx.doi.org/10.3390/s24154819.

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Gaikwad, Pratibhahanmantrao, i Dhiren Pranshankar Dave. "Remote Sensing with Internet Based Patient Condition Observing System". Indonesian Journal of Electrical Engineering and Computer Science 9, nr 3 (1.03.2018): 629. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp629-632.

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This Paper aims to design and demonstrate an innovative web-based remote healthcare diagnostic system that provides vital medical data and live video images of a patient situated in the rural area accessible to a health professional available elsewhere in urban centres resulting in better diagnosis and treatment of that patient.

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Jiang, Kui, Zhongyuan Wang, Peng Yi, Junjun Jiang, Jing Xiao i Yuan Yao. "Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution". Remote Sensing 10, nr 11 (29.10.2018): 1700. http://dx.doi.org/10.3390/rs10111700.

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Deep convolutional neural networks (CNNs) have been widely used and achieved state-of-the-art performance in many image or video processing and analysis tasks. In particular, for image super-resolution (SR) processing, previous CNN-based methods have led to significant improvements, when compared with shallow learning-based methods. However, previous CNN-based algorithms with simple direct or skip connections are of poor performance when applied to remote sensing satellite images SR. In this study, a simple but effective CNN framework, namely deep distillation recursive network (DDRN), is presented for video satellite image SR. DDRN includes a group of ultra-dense residual blocks (UDB), a multi-scale purification unit (MSPU), and a reconstruction module. In particular, through the addition of rich interactive links in and between multiple-path units in each UDB, features extracted from multiple parallel convolution layers can be shared effectively. Compared with classical dense-connection-based models, DDRN possesses the following main properties. (1) DDRN contains more linking nodes with the same convolution layers. (2) A distillation and compensation mechanism, which performs feature distillation and compensation in different stages of the network, is also constructed. In particular, the high-frequency components lost during information propagation can be compensated in MSPU. (3) The final SR image can benefit from the feature maps extracted from UDB and the compensated components obtained from MSPU. Experiments on Kaggle Open Source Dataset and Jilin-1 video satellite images illustrate that DDRN outperforms the conventional CNN-based baselines and some state-of-the-art feature extraction approaches.

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Patra, Anirban, Mainuck Das, Anirban Ghosal, Aniruddha Ghosh, Indranil Kushary, Samiran Roy i Debasish Chakraborty. "Remote Sensing Image Encryption and Error Detection using Hamming Code". Journal of Physics: Conference Series 2286, nr 1 (1.07.2022): 012018. http://dx.doi.org/10.1088/1742-6596/2286/1/012018.

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Abstract Encryption of remote sensing images is always required as this type of images carry sensitive data. It is the primary criteria to hide it in case of a country’s border image or video. Nowadays development in networking improves communication systems. Unfortunately, intruders are misusing this advantage and try to intrude into the network whose security is weak. During secure communication of remote sensing images, encryption is done before sending to a long distance. Here, we have discussed a new method of remote sensing image encoding with the help of hamming code. During analyzation, we have showed that it is not easy to decode the main image from encrypted data in the most popular mode of attacking- known plaintext attack. Moreover, we have also calculated that this system can detect the error in an effective way. The presented method is also applicable for effective encryption of more than one remote sensing images.

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Zhang, Yu, Lingfei Wang, Chenghao Zhang i Jin Li. "Adversarial Examples in Visual Object Tracking in Satellite Videos: Cross-Frame Momentum Accumulation for Adversarial Examples Generation". Remote Sensing 15, nr 13 (23.06.2023): 3240. http://dx.doi.org/10.3390/rs15133240.

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The visual object tracking technology of remote sensing images has important applications in areas with high safety performance such as national defense, homeland security, and intelligent transportation in smart cities. However, previous research demonstrates that adversarial examples pose a significant threat to remote sensing imagery. This article first explores the impact of adversarial examples in the field of visual object tracking in remote sensing imagery. We design a classification- and regression-based loss function for the popular Siamese RPN series of visual object tracking models and use the PGD gradient-based attack method to generate adversarial examples. Additionally, we consider the temporal consistency of video frames and design an adversarial examples attack method based on momentum continuation. We evaluate our method on the remote sensing visual object tracking datasets SatSOT and VISO and the traditional datasets OTB100 and UAV123. The experimental results show that our approach can effectively reduce the performance of the tracker.

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WANG, Yaoli, Xiaohui LIU, Bin LI i Qing CHANG. "The manifold embedded selective pseudo-labeling algorithm and transfer learning of small sample dataset". Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, nr 5 (październik 2021): 1122–29. http://dx.doi.org/10.1051/jnwpu/20213951122.

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Special scene classification and identification tasks are not easily fulfilled to obtain samples, which results in a shortage of samples. The focus of current researches lies in how to use source domain data (or auxiliary domain data) to build domain adaption transfer learning models and to improve the classification accuracy and performance of small sample machine learning in these special and difficult scenes. In this paper, a model of deep convolution and Grassmann manifold embedded selective pseudo-labeling algorithm (DC-GMESPL) is proposed to enable transfer learning classifications among multiple small sample datasets. Firstly, DC-GMESPL algorithm uses satellite remote sensing image sample data as the source domain to extract the smoke features simultaneously from both the source domain and the target domain based on the Resnet50 deep transfer network. This is done for such special scene of the target domain as the lack of local sample data for forest fire smoke video images. Secondly, DC-GMESPL algorithm makes the source domain feature distribution aligned with the target domain feature distribution. The distance between the source domain and the target domain feature distribution is minimized by removing the correlation between the source domain features and re-correlation with the target domain. And then the target domain data is pseudo-labeled by selective pseudo-labeling algorithm in Grassmann manifold space. Finally, a trainable model is constructed to complete the transfer classification between small sample datasets. The model of this paper is evaluated by transfer learning between satellite remote sensing image and video image datasets. Experiments show that DC-GMESPL transfer accuracy is higher than DC-CMEDA, Easy TL, CMMS and SPL respectively. Compared with our former DC-CMEDA, the transfer accuracy of our new DC-GMESPL algorithm has been further improved. The transfer accuracy of DC-GMESPL from satellite remote sensing image to video image has been improved by 0.50%, the transfer accuracy from video image to satellite remote sensing image has been improved by 8.50% and then, the performance has been greatly improved.

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Gawehn, Matthijs, Sierd de Vries i Stefan Aarninkhof. "A Self-Adaptive Method for Mapping Coastal Bathymetry On-The-Fly from Wave Field Video". Remote Sensing 13, nr 23 (23.11.2021): 4742. http://dx.doi.org/10.3390/rs13234742.

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Mapping coastal bathymetry from remote sensing becomes increasingly more attractive for the coastal community. It is facilitated by a rising availability of drone and satellite data, advances in data science, and an open-source mindset. Coastal bathymetry, but also wave directions, celerity and near-surface currents can simultaneously be derived from aerial video of a wave field. However, the required video processing is usually extensive, requires skilled supervision, and is tailored to a fieldsite. This study proposes a video-processing algorithm that resolves these issues. It automatically adapts to the video data and continuously returns mapping updates and thereby aims to make wave-based remote sensing more inclusive to the coastal community. The code architecture for the first time includes the dynamic mode decomposition (DMD) to reduce the data complexity of wavefield video. The DMD is paired with loss-functions to handle spectral noise and a novel spectral storage system and Kalman filter to achieve fast converging measurements. The algorithm is showcased for fieldsites in the USA, the UK, the Netherlands, and Australia. The performance with respect to mapping bathymetry was validated using ground truth data. It was demonstrated that merely 32 s of video footage is needed for a first mapping update with average depth errors of 0.9–2.6 m. These further reduced to 0.5–1.4 m as the videos continued and more mapping updates were returned. Simultaneously, coherent maps for wave direction and celerity were achieved as well as maps of local near-surface currents. The algorithm is capable of mapping the coastal parameters on-the-fly and thereby offers analysis of video feeds, such as from drones or operational camera installations. Hence, the innovative application of analysis techniques like the DMD enables both accurate and unprecedentedly fast coastal reconnaissance. The source code and data of this article are openly available.

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Zheng, Huanhuan, Yuxiu Bai i Yurun Tian. "A Multi Moving Target Recognition Algorithm Based on Remote Sensing Video". Computer Modeling in Engineering & Sciences 131, nr 2 (2022): 1–13. http://dx.doi.org/10.32604/cmes.2022.020995.

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du Plessis, L. A. "NEW TECHNOLOGY IN FLOOD DAMAGE ASSESSMENT: A VIDEO REMOTE SENSING APPROACH". Agrekon 38, nr 3 (wrzesień 1999): 302–20. http://dx.doi.org/10.1080/03031853.1999.9523556.

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ZHANG, Da, Shu-yan XU i Qing-ju MENG. "Research on high-speed TDICCD remote sensing camera video signal processing". Journal of China Universities of Posts and Telecommunications 16, nr 3 (czerwiec 2009): 95–102. http://dx.doi.org/10.1016/s1005-8885(08)60233-2.

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Neale, Christopher M. U., i Blake G. Crowther. "An airborne multispectral video/radiometer remote sensing system: Development and calibration". Remote Sensing of Environment 49, nr 3 (wrzesień 1994): 187–94. http://dx.doi.org/10.1016/0034-4257(94)90014-0.

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Li, Ronghao, Pengqi Gao, Xiangyuan Cai, Xiaotong Chen, Jiangnan Wei, Yinqian Cheng i Hongying Zhao. "A Real-Time Incremental Video Mosaic Framework for UAV Remote Sensing". Remote Sensing 15, nr 8 (18.04.2023): 2127. http://dx.doi.org/10.3390/rs15082127.

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Unmanned aerial vehicles (UAVs) are becoming increasingly popular in various fields such as agriculture, forest protection, resource exploration, and so on, due to their ability to capture high-resolution images quickly and efficiently at low altitudes. However, real-time image mosaicking of UAV image sequences, especially during long multi-strip flights, remains challenging. In this paper, a real-time incremental UAV image mosaicking framework is proposed, which only uses the UAV image sequence, and does not rely on global positioning system (GPS), ground control points (CGPs), or other auxiliary information. Our framework aims to reduce spatial distortion, increase the speed of the operation in the mosaicking process, and output high-quality panorama. To achieve this goal, we employ several strategies. First, the framework estimates the approximate position of each newly added frame and selects keyframes to improve efficiency. Then, the matching relationship between keyframes and other frames is obtained by using the estimated position. After that, a new optimization method based on minimizing weighted reprojection errors is adopted to carry out precise position calculation of the current frame, so as to reduce the deformation caused by cumulative errors. Finally, the weighted partition fusion method based on the Laplacian pyramid is used to fuse and update the local image in real time to achieve the best mosaic result. We have carried out a series of experiments which show that our system can output high-quality panorama in real time. The proposed keyframe selection strategy and local optimization strategy can minimize cumulative errors, the image fusion strategy is highly robust, and it can effectively improve the panorama quality.

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Linden, David S. "Videography for Foresters". Journal of Forestry 98, nr 6 (1.06.2000): 25–27. http://dx.doi.org/10.1093/jof/98.6.25.

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Abstract Many types of video cameras and video systems are used by remote sensing specialists. Cameras can be black-and-white, color, or even color-infrared. Although not a substitute for aerial photography or satellite images, videography is a fast and inexpensive way to obtain images from the air for certain applications. Combined with geographic positioning system data, video images can be quickly and easily integrated with GIS.

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Li, Sheng-liang, Kun Liu, Feng Zhang, Long-long Xiao i Da-Peng Han. "Joint L1/Lp-regularized minimization in video recovery of remote sensing based on compressed sensing". Optik 125, nr 23 (grudzień 2014): 7080–84. http://dx.doi.org/10.1016/j.ijleo.2014.08.098.

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Yu, Chaoran, Zhejun Feng, Zengyan Wu, Runxi Wei, Baoming Song i Changqing Cao. "HB-YOLO: An Improved YOLOv7 Algorithm for Dim-Object Tracking in Satellite Remote Sensing Videos". Remote Sensing 15, nr 14 (14.07.2023): 3551. http://dx.doi.org/10.3390/rs15143551.

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The You Only Look Once (YOLO) series has been widely adopted across various domains. With the increasing prevalence of continuous satellite observation, the resulting video streams can be subjected to intelligent analysis for various applications, such as traffic flow statistics, military operations, and other fields. Nevertheless, the signal-to-noise ratio of objects in satellite videos is considerably low, and their size is often smaller, ranging from tens to one percent, when compared to those taken by drones and other equipment. Consequently, the original YOLO algorithm’s performance is inadequate when detecting tiny objects in satellite videos. Hence, we propose an improved framework, named HB-YOLO. To enable the backbone to extract features, we replaced the universal convolution with an improved HorNet that enables higher-order spatial interactions. We replaced all Extended Efficient Layer Aggregation Networks (ELANs) with the BoTNet attention mechanism to make the features fully fused. In addition, anchors were re-adjusted, and image segmentation was integrated to achieve detection results, which are tracked using the BoT-SORT algorithm. The experimental results indicate that the original algorithm failed to learn using the satellite video dataset, whereas our proposed approach yielded improved recall and precision. Specifically, the F1-score and mean average precision increased to 0.58 and 0.53, respectively, and the object-tracking performance was enhanced by incorporating the image segmentation method.

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Boesch, R. "THERMAL REMOTE SENSING WITH UAV-BASED WORKFLOWS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (23.08.2017): 41–46. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-41-2017.

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Climate change will have a significant influence on vegetation health and growth. Predictions of higher mean summer temperatures and prolonged summer draughts may pose a threat to agriculture areas and forest canopies. Rising canopy temperatures can be an indicator of plant stress because of the closure of stomata and a decrease in the transpiration rate. Thermal cameras are available for decades, but still often used for single image analysis, only in oblique view manner or with visual evaluations of video sequences. Therefore remote sensing using a thermal camera can be an important data source to understand transpiration processes. Photogrammetric workflows allow to process thermal images similar to RGB data. But low spatial resolution of thermal cameras, significant optical distortion and typically low contrast require an adapted workflow. Temperature distribution in forest canopies is typically completely unknown and less distinct than for urban or industrial areas, where metal constructions and surfaces yield high contrast and sharp edge information. The aim of this paper is to investigate the influence of interior camera orientation, tie point matching and ground control points on the resulting accuracy of bundle adjustment and dense cloud generation with a typically used photogrammetric workflow for UAVbased thermal imagery in natural environments.

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Li, Sujuan, i Shichen Huang. "Remote medical video region tamper detection system based on Wireless Sensor Network". EAI Endorsed Transactions on Pervasive Health and Technology 8, nr 31 (26.07.2022): e3. http://dx.doi.org/10.4108/eetpht.v8i31.702.

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INTRODUCTION: A new telemedicine video tamper detection system based on wireless sensor network is proposed and designed in this paper. OBJECTIVES: This work is proposed to improve the performance of telemedicine video communication and accurately detect the tamper area in telemedicine video. METHODS: The sensor nodes in the sensing layer are responsible for collecting telemedicine video information and transmitting the information to the data layer. The data layer completes the storage of information and transmits it to the processing layer. The detection module of the processing layer detects the tampered area of the telemedicine video through two parts: suspicious moving point calculation and tamper detection, and transmits the detection results to the application display layer for display. RESULTS: The experimental results show that the designed detection system can accurately detect the tampered area in the telemedicine video, and the packet loss rate is significantly reduced, and the maximum packet loss rate is no more than 1%. CONCLUSION: The proposed detection system for remote medical video based on wireless sensor network can better meet the requirements of region tamper detection.

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Everitt, James H., Gerald L. Anderson, David E. Escobar, Michael R. Davis, Neal R. Spencer i Roger J. Andrascik. "Use of Remote Sensing for Detecting and Mapping Leafy Spurge (Euphorbia esula)". Weed Technology 9, nr 3 (wrzesień 1995): 599–609. http://dx.doi.org/10.1017/s0890037x00023915.

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Leafy spurge is a troublesome, exotic weed in the northern Great Plains of the United States. Leafy spurge produces showy yellow bracts during June that give this weed a conspicuous appearance. A study was conducted to determine the feasibility of using remote sensing techniques to detect leafy spurge in this phenological stage. Study sites were located in North Dakota and Montana. Plant canopy reflectance measurements showed that leafy spurge had higher visible (0.63- to 0.69-μm) reflectance than several associated plant species. The conspicuous yellow bracts of leafy spurge gave it distinct yellow-green and pink images on conventional color and color-infrared aerial photographs, respectively. Leafy spurge also could be distinguished on conventional color video imagery where it had a golden yellow image response. Quantitative data obtained from digitized video images showed that leafy spurge had statistically different digital values from those of associated vegetation and soil. Computer analyses of video images showed/that light reflected from leafy spurge populations could be quantified from associated vegetation. This technique permits area estimates of leafy spurge populations. Large format conventional color photographs of Theodore Roosevelt National Park near Medora, ND were digitized and integrated with a geographic information system to produce a map of leafy spurge populations within the park that can be useful to monitor the spread or decline of leafy spurge.

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Chen, Sinan, Masahide Nakamura i Kenji Sekiguchi. "Consecutive and Effective Facial Masking Using Image-Based Bone Sensing for Remote Medicine Education". Applied Sciences 12, nr 20 (18.10.2022): 10507. http://dx.doi.org/10.3390/app122010507.

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Unlike masking human faces from images, facial masking in real-time, frame by frame from a video stream, presents technical challenges related to various factors such as camera-to-human distance, head direction, and mosaic schemes. In many existing studies, expensive equipment and huge computational resources are strongly required, and it is not easy to effectively realize real-time facial masking with a simpler approach. This study aims to develop a secure streaming system to support remote medicine education and to quantitatively evaluate consecutive and effective facial masking using image-based bone sensing. Our key idea is to use the facial feature of bone sensing instead of general face recognition techniques to perform facial masking from the video stream. We use a general-purpose computer and a USB fixed-point camera to implement the eye line mosaic and face mosaic. We quantitatively evaluate the results of facial masking at different distances and human head orientations using bone sensing technology and a depth camera. we compare the results of a similar approach for face recognition with those of bone sensing. As the main results, consecutive face masking using bone sensing is unaffected by distance and head orientation, and the variation width of the mosaic area is stable within around 30% of the target area. However, about three-fourths of the results using conventional face recognition were unable to mask their faces consecutively.

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Cressie, Noel, i Andrew B. Lawson. "Hierarchical probability models and Bayesian analysis of mine locations". Advances in Applied Probability 32, nr 2 (czerwiec 2000): 315–30. http://dx.doi.org/10.1239/aap/1013540165.

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Based on remote sensing of a potential minefield, point locations are identified, some of which may not be mines. The mines and mine-like objects are to be distinguished based on their point patterns, although it must be emphasized that all one sees is the superposition of their locations. In this paper, we construct a hierarchical spatial point-process model that accounts for the different patterns of mines and mine-like objects and uses posterior analysis to distinguish between them. Our Bayesian approach is applied to minefield data obtained from a multispectral video remote-sensing system.

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Cressie, Noel, i Andrew B. Lawson. "Hierarchical probability models and Bayesian analysis of mine locations". Advances in Applied Probability 32, nr 02 (czerwiec 2000): 315–30. http://dx.doi.org/10.1017/s0001867800009940.

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Based on remote sensing of a potential minefield, point locations are identified, some of which may not be mines. The mines and mine-like objects are to be distinguished based on their point patterns, although it must be emphasized that all one sees is the superposition of their locations. In this paper, we construct a hierarchical spatial point-process model that accounts for the different patterns of mines and mine-like objects and uses posterior analysis to distinguish between them. Our Bayesian approach is applied to minefield data obtained from a multispectral video remote-sensing system.

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Rahman, Munshi K., Thomas W. Schmidlin, Mandy J. Munro-Stasiuk i Andrew Curtis. "Geospatial Analysis of Land Loss, Land Cover Change, and Landuse Patterns of Kutubdia Island, Bangladesh". International Journal of Applied Geospatial Research 8, nr 2 (kwiecień 2017): 45–60. http://dx.doi.org/10.4018/ijagr.2017040104.

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This study utilizes geospatial tools of remote sensing, geographical information systems (GIS), and global positioning system (GPS) to examine the land loss, land cover (LC) change, landuse of Kutubdia Island, Bangladesh. Multi-spectral Scanner (MSS), Thematic Mapper (TM), and Landsat8 OLI imageries were used for land cover change. For assessing the landuse patterns of 2012, spatial video data were collected by using contour GPS camera. Using remote sensing analysis three different land cover classes (water, trees and forest, and agriculture) were identified and land cover changes were detected from 1972 to 2013. The results show from 1972 to 2013, an estimated 9 km2 of land has been lost and significant changes have taken place from 1972 to 2013. Only an estimated .35 km2 area of accretion has taken place during the study period. Using GIS eight different landuse patterns were identified based on spatial video data.

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Li, Jing, Hongwu Hu, Lilan Lei i Jin Li. "Remote Sensing Image Fusion Method Based on Progressive Cascaded Deep Residual Network". Wireless Communications and Mobile Computing 2023 (22.02.2023): 1–9. http://dx.doi.org/10.1155/2023/7793444.

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With the rapid development of deep learning in recent years, it has shown excellent performance in various image and video processing tasks. In addition, it also has a great role in promoting the spatio-temporal fusion of remote sensing images. The reconstructed image can give people a good visual experience. The invention relates to a remote sensing image fusion method based on a progressive cascade deep residual network and provides an end-to-end progressive cascade deep residual network model for remote sensing image fusion. The use of the MSE loss function may cause oversmoothing of the fused image, so a new joint loss function is defined to capture finer spatial information to improve the spatial resolution of the fused image. Resize-convolution is used to replace the transposed convolution to eliminate the checkerboard effect in the fused image caused by the transposed convolution. Through the experiments on the remote sensing image fusion simulation and real datasets of multiple satellites, the data results of the proposed algorithm are more than 5.25% better than those of the comparative algorithm in the average quantification. The calculation time and system resource occupation are also reduced, which has important theoretical significance and application value in the field of artificial intelligence and image processing. It will play a certain role in promoting the theoretical research and application of remote sensing image fusion.

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Yang, Yang, Guangmin Sun, Dequn Zhao i Bo Peng. "A Real Time Mosaic Method for Remote Sensing Video Images from UAV". Journal of Signal and Information Processing 04, nr 03 (2013): 168–72. http://dx.doi.org/10.4236/jsip.2013.43b030.

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Mehmood, Maryam, Ahsan Shahzad, Bushra Zafar, Amsa Shabbir i Nouman Ali. "Remote Sensing Image Classification: A Comprehensive Review and Applications". Mathematical Problems in Engineering 2022 (2.08.2022): 1–24. http://dx.doi.org/10.1155/2022/5880959.

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Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

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Xiu, Hongling, i Fengyun Yang. "Batch Processing of Remote Sensing Image Mosaic based on Python". International Journal of Online Engineering (iJOE) 14, nr 09 (30.09.2018): 208. http://dx.doi.org/10.3991/ijoe.v14i09.9226.

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In the process of remote sensing image processing, analysis and interpretation, it is usually necessary to combine several local images into a complete image. Aiming at the shortcoming of long and complicated process of conventional semi-automatic video stitching. In this paper, using the splicing method of pixels, based on the Python interface of ArcGIS 10.1 platform, the idea of programming language is introduced and batch mosaic of remote sensing images is realized. Through the comparison with the image processing software, it is found that this method can shorten the time of image mosaic and improve the efficiency of splicing, which is convenient for later image analysis and other work under the premise of ensuring the accuracy.

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Wu, Di, Haibo Song i Caizhi Fan. "Object Tracking in Satellite Videos Based on Improved Kernel Correlation Filter Assisted by Road Information". Remote Sensing 14, nr 17 (26.08.2022): 4215. http://dx.doi.org/10.3390/rs14174215.

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Video satellites can stare at target areas on the Earth’s surface to obtain high-temporal-resolution remote sensing videos, which make it possible to track objects in satellite videos. However, it should be noted that the object size in satellite videos is usually small and has less textural property, and the moving objects in satellite videos are easily occluded, which puts forward higher requirements for the tracker. In order to solve the above problems, consider that the remote sensing image contains rich road information, which can be used to constrain the trajectory of the object in a satellite video, this paper proposes an improved Kernel Correlation Filter (KCF) assisted by road information to track small objects, especially when the object is occluded. Specifically, the contributions of this paper are as follows: First, the tracking confidence module is reconstructed, which integrates the peak response and the average peak correlation energy of the response map to more accurately judge whether the object is occluded. Then, an adaptive Kalman filter is designed to adaptively adjust the parameters of the Kalman filter according to the motion state of the object, which improves the robustness of tracking and reduces the tracking drift after the object is occluded. Last but not least, an object tracking strategy assisted by road information is recommended, which searches for objects with road information as constraints, to locate objects more accurately. After the above improvements, compared with the KCF tracker, our method improves the tracking precision by 35.9% and the tracking success rate by 18.1% with the tracking rate at a speed of 300 frames per second, which meets the real-time requirements.

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Jiang, Yan, i Guisheng Yin. "Attention-Enhanced One-Shot Attack against Single Object Tracking for Unmanned Aerial Vehicle Remote Sensing Images". Remote Sensing 15, nr 18 (14.09.2023): 4514. http://dx.doi.org/10.3390/rs15184514.

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Recent studies have shown that deep-learning-based models for processing Unmanned Aerial Vehicle (UAV) remote sensing images are vulnerable to artificially designed adversarial examples, which can lead to incorrect predictions of deep models when facing adversarial examples. Previous adversarial attack methods have mainly focused on the classification and detection of UAV remote sensing images, and there is still a lack of research on adversarial attacks for object tracking in UAV video. To address this challenge, we propose an attention-enhanced one-shot adversarial attack method for UAV remote sensing object tracking, which perturbs only the template frame and generates adversarial samples offline. First, we employ an attention feature loss to make the original frame’s features dissimilar to those of the adversarial frame, and an attention confidence loss to either suppress or enhance different confidence scores. Additionally, by forcing the tracker to concentrate on the background information near the target, a background distraction loss is used to mismatch templates with subsequent frames. Finally, we add total variation loss to generate adversarial examples that appear natural to humans. We validate the effectiveness of our method against popular trackers such as SiamRPN, DaSiamRPN, and SiamRPN++ on the UAV123 remote sensing dataset. Experimental results verify the superior attack performance of our proposed method.

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Chan, Angela, Francis Quek, Haard Panchal, Joshua Howell, Takashi Yamauchi i Jinsil Hwaryoung Seo. "The Effect of Co-Verbal Remote Touch on Electrodermal Activity and Emotional Response in Dyadic Discourse". Sensors 21, nr 1 (29.12.2020): 168. http://dx.doi.org/10.3390/s21010168.

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This article explores the affective impact of remote touch when used in conjunction with video telecon. Committed couples were recruited to engage in semi-structured discussions after they watched a video clip that contained emotionally charged moments. They used paired touch input and output devices to send upper-arm squeezes to each other in real-time. Users were not told how to use the devices and were free to define the purpose of their use. We examined how remote touch was used and its impact on skin conductance and affective response. We observed 65 different touch intents, which were classified into broader categories. We employed a series of analyses within a framework of behavioral and experiential timescales. Our findings revealed that remote touches created a change in the overall psychological affective experience and skin conductance response. Only remote touches that were judged to be affective elicited significant changes in EDA measurements. Our study demonstrates the affective power of remote touch in video telecommunication, and that off-the-shelf wearable EDA sensing devices can detect such affective impacts. Our findings pave the way for new species of technologies with real-time feedback support for a range of communicative and special needs such as isolation, stress, and anxiety.

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