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Journal articles on the topic 'Pattern recognition; Radar data'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Trafalis, Theodore B., and Anderson White. "Data Mining Techniques for Pattern Recognition: Tornado Signatures in Doppler Weather Radar Data." International Journal of Smart Engineering System Design 5, no. 4 (2003): 347–59. http://dx.doi.org/10.1080/10255810390224107.

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2

Bianco, Laura, Daniel Gottas, and James M. Wilczak. "Implementation of a Gabor Transform Data Quality-Control Algorithm for UHF Wind Profiling Radars." Journal of Atmospheric and Oceanic Technology 30, no. 12 (2013): 2697–703. http://dx.doi.org/10.1175/jtech-d-13-00089.1.

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Abstract In this paper a Gabor transform–based algorithm is applied to identify and eliminate intermittent signal contamination in UHF wind profiling radars, such as that produced by migrating birds. The algorithm is applied in the time domain, and so it can be used to improve the accuracy of UHF radar wind profiler data in real time—an essential requirement if these wind profiler data are to be assimilated into operational weather forecast models. The added value of using a moment-level Weber–Wuertz pattern recognition scheme that follows the Gabor transform processing is demonstrated.

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Ahmed, Shahzad, and Sung Ho Cho. "Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier." Sensors 20, no. 2 (2020): 564. http://dx.doi.org/10.3390/s20020564.

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The emerging integration of technology in daily lives has increased the need for more convenient methods for human–computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man–machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstra

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Maas, Christian, and Jörg Schmalzl. "Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar." Computers & Geosciences 58 (August 2013): 116–25. http://dx.doi.org/10.1016/j.cageo.2013.04.012.

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5

Chu, Chen-Chau, N. Nandhakumar, and J. K. Aggarwal. "Image segmentation using laser radar data." Pattern Recognition 23, no. 6 (1990): 569–81. http://dx.doi.org/10.1016/0031-3203(90)90035-j.

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6

Li, Jia, Xiumin Chu, Wei He, Feng Ma, Reza Malekian, and Zhixiong Li. "A Generalised Bayesian Inference Method for Maritime Surveillance Using Historical Data." Symmetry 11, no. 2 (2019): 188. http://dx.doi.org/10.3390/sym11020188.

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In practice, maritime monitoring systems rely on manual work to identify the authenticities, risks, behaviours and importance of moving objects, which cannot be obtained directly through sensors, especially from marine radar. This paper proposes a generalised Bayesian inference-based artificial intelligence that is capable of identifying these patterns of moving objects based on their dynamic attributes and historical data. First of all, based on dependable prior data, likelihood information about objects of interest is obtained in terms of dynamic attributes, such as speed, direction and posi

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Kim, Seong-Hoon, Zong Woo Geem, and Gi-Tae Han. "A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor." Sensors 19, no. 15 (2019): 3340. http://dx.doi.org/10.3390/s19153340.

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Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be difficult due to the inconvenience and sensitivity of physical contact. In recent years, research has been focused on using sensors such as Ultra-wideband Radar, which can acquire bio-signals even in a non-contact environment, to solve these problems. In this paper, we have acquired respiratory signal d

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Austin, G. L., A. Bellon, M. Riley, and E. Ballantyne. "Navigation by Computer Processing of Marine Radar Images." Journal of Navigation 38, no. 3 (1985): 375–83. http://dx.doi.org/10.1017/s0373463300032744.

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The advantages of being able to process marine radar imagery in an on-line computer system have been illustrated by study of some navigational problems. The experiments suggest that accuracies of the order of 100 metres may be obtained in navigation in coastal regions using map overlays with marine radar data. A similar technique using different radar imagery of the same location suggests that the pattern-recognition technique may well yield a position-keeping ability of better than 10 metres.

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9

LIAO, XUEJUN, and ZHENG BAO. "RADAR TARGET RECOGNITION BASED ON PARAMETERIZED HIGH RESOLUTION RANGE PROFILES." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 07 (2000): 979–86. http://dx.doi.org/10.1142/s0218001400000623.

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A new scheme of radar target recognition based on parameterized high resolution range profiles (PHRRP) is presented in this paper. A novel criterion called generalized-weighted-normalized correlation (GWNC) is proposed for measuring the similarity between PHRRP's. By properly choosing the parameter of the mainlobe width in GWNC, aspect sensitivity of PHRRP's can be reduced without sacrificing their discriminative power. Performance of the scheme is evaluated using a dataset of three scaled aircraft models. The experimental results show that by using GWNC, only a small number of most dominant s

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Milisavljević, Nada, Isabelle Bloch, Sebastiaan van den Broek, and Marc Acheroy. "Improving mine recognition through processing and Dempster–Shafer fusion of ground-penetrating radar data." Pattern Recognition 36, no. 5 (2003): 1233–50. http://dx.doi.org/10.1016/s0031-3203(02)00251-0.

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11

Peters, J. F., Z. Suraj, S. Shan, S. Ramanna, W. Pedrycz, and N. Pizzi. "Classification of meteorological volumetric radar data using rough set methods." Pattern Recognition Letters 24, no. 6 (2003): 911–20. http://dx.doi.org/10.1016/s0167-8655(02)00203-9.

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12

Yeste, Luis Miguel, Saturnina Henares, Neil McDougall, Fernando García-García, and César Viseras. "Towards the multi-scale characterization of braided fluvial geobodies from outcrop, core, ground-penetrating radar and well log data." Geological Society, London, Special Publications 488, no. 1 (2018): 73–95. http://dx.doi.org/10.1144/sp488.3.

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AbstractThe integrated application of advanced visualization techniques – validated against outcrop, core and gamma ray log data – was found to be crucial in characterizing the spatial distribution of fluvial facies and their inherent permeability baffles to a centimetre-scale vertical resolution. An outcrop/behind outcrop workflow was used, combining the sedimentological analysis of a perennial deep braided outcrop with ground-penetrating radar profiles, behind outcrop optical and acoustic borehole imaging, and the analyses of dip tadpoles, core and gamma ray logs. Data from both the surface

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13

Rignot, E., and R. Chellappa. "Segmentation of synthetic-aperture-radar complex data." Journal of the Optical Society of America A 8, no. 9 (1991): 1499. http://dx.doi.org/10.1364/josaa.8.001499.

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14

Zaugg, Serge, Gilbert Saporta, Emiel van Loon, Heiko Schmaljohann, and Felix Liechti. "Automatic identification of bird targets with radar via patterns produced by wing flapping." Journal of The Royal Society Interface 5, no. 26 (2008): 1041–53. http://dx.doi.org/10.1098/rsif.2007.1349.

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Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extra

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15

Li, Cheng, Wei Wang, Longfei Shi, and Xuesong Wang. "Recognition and Parameter Extraction of One-Dimensional Electronic Scanning for 3D Radar." International Journal of Antennas and Propagation 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/567954.

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Most of the existing antenna scan type (AST) identification techniques focus on only mechanical scanning (MS) and rarely research electronic scanning (ES), especially one-dimensional (1D) ES. This paper proposes a recognition and parameter extraction method of 1D ES for 3D radar, which employs both MS and ES. An ES pattern simulator is designed to synthesize pulse amplitude (PA) data first. Then 1D ES is distinguished from MS and two-dimensional (2D) ES based on the features extracted from the difference sequences of beam sequence, such as resemblance coefficient (RC) and variance. Subsequentl

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16

Morgera, Salvatore D. "Radar data processing — volume I: Introduction and tracking." Signal Processing 11, no. 4 (1986): 413–15. http://dx.doi.org/10.1016/0165-1684(86)90084-8.

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17

Cuchi, Kazuo. "Multi-look processing of synthetic aperture radar data from dynamic ocean surfaces." Pattern Recognition Letters 4, no. 4 (1986): 305–14. http://dx.doi.org/10.1016/0167-8655(86)90012-7.

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18

Morgera, S. D. "Radar data processing, vol. II: Advanced topics and applications." Signal Processing 13, no. 3 (1987): 331–32. http://dx.doi.org/10.1016/0165-1684(87)90132-0.

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19

ROLI, F. "MULTISENSOR IMAGE RECOGNITION BY NEURAL NETWORKS WITH UNDERSTANDABLE BEHAVIOR." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 08 (1996): 887–917. http://dx.doi.org/10.1142/s0218001496000517.

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Recently, a kind of structured neural networks (SNNs) explicitly devoted to multisensor image recognition and aimed at allowing the interpretation of the "network behavior" was presented in Ref. 1. Experiments reported in Ref. 1 pointed out that SNNs provide a trade-off between recognition accuracy and interpretation of the network behavior. In this paper, the combination of multiple SNNs, each of which has been trained on the same data set, is proposed as a means to improve recognition results, while keeping the possibility of interpreting the network behavior. A simple method for interpretin

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20

Hofmann, U., A. Rieder, and E. D. Dickmanns. "Radar and vision data fusion for hybrid adaptive cruise control on highways." Machine Vision and Applications 14, no. 1 (2003): 42–49. http://dx.doi.org/10.1007/s00138-002-0093-y.

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21

Ren, Jianfeng, and Xudong Jiang. "A three-step classification framework to handle complex data distribution for radar UAV detection." Pattern Recognition 111 (March 2021): 107709. http://dx.doi.org/10.1016/j.patcog.2020.107709.

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22

Yu, Tian-You, Yadong Wang, Alan Shapiro, Mark B. Yeary, Dusan S. Zrnić, and Richard J. Doviak. "Characterization of Tornado Spectral Signatures Using Higher-Order Spectra." Journal of Atmospheric and Oceanic Technology 24, no. 12 (2007): 1997–2013. http://dx.doi.org/10.1175/2007jtecha934.1.

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Abstract Distinct tornado spectral signatures (TSSs), which are similar to white noise spectra or have bimodal features, have been observed in both simulations and real data from Doppler radars. The shape of the tornado spectrum depends on several parameters such as the range of the tornado, wind field within the storm, and the reflectivity structure. In this work, one of the higher-order spectra (HOS), termed bispectrum, is implemented to characterize TSS, in which the Doppler spectrum is considered a 1D pattern. Bispectrum has been successfully applied to pattern recognition in other fields

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23

Winterrath, T., and W. Rosenow. "A new module for the tracking of radar-derived precipitation with model-derived winds." Advances in Geosciences 10 (April 26, 2007): 77–83. http://dx.doi.org/10.5194/adgeo-10-77-2007.

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Abstract. A new approach for the nowcasting of precipitation has been developed at the German Weather Service combining extrapolation techniques and Numerical Weather Prediction (NWP) for a lead time range of several hours. Radar-derived precipitation fields serve as input data for a tracking algorithm using model-derived wind data. The composite precipitation field is derived from the precipitation scans which are performed every five minutes at the 16 German radar stations. The data are corrected from clutter and shading effects. The tracking of this radar-derived precipitation field is perf

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24

Fowler, Mark L., Mo Chen, J. Andrew Johnson, and Zhen Zhou. "Data compression using SVD and Fisher information for radar emitter location." Signal Processing 90, no. 7 (2010): 2190–202. http://dx.doi.org/10.1016/j.sigpro.2010.01.026.

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25

Jordanov, Ivan, Nedyalko Petrov, and Alessio Petrozziello. "Classifiers Accuracy Improvement Based on Missing Data Imputation." Journal of Artificial Intelligence and Soft Computing Research 8, no. 1 (2018): 31–48. http://dx.doi.org/10.1515/jaiscr-2018-0002.

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Abstract In this paper we investigate further and extend our previous work on radar signal identification and classification based on a data set which comprises continuous, discrete and categorical data that represent radar pulse train characteristics such as signal frequencies, pulse repetition, type of modulation, intervals, scan period, scanning type, etc. As the most of the real world datasets, it also contains high percentage of missing values and to deal with this problem we investigate three imputation techniques: Multiple Imputation (MI); K-Nearest Neighbour Imputation (KNNI); and Bagg

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26

Maciel, Susanne, and Ricardo Biloti. "A statistics-based descriptor for automatic classification of scatterers in seismic sections." GEOPHYSICS 85, no. 5 (2020): O83—O96. http://dx.doi.org/10.1190/geo2018-0673.1.

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Discontinuities and small structures induce diffractions on seismic or ground-penetrating radar (GPR) acquisitions. Therefore, diffraction images can be used as a tool to access valuable information concerning subsurface scattering features, such as pinch outs, fractures, and edges. Usually, diffraction-imaging methods operate on diffraction events previously detected. Pattern-recognition methods are efficient to detect, image, and characterize diffractions. The use of this kind of approach, though, requires a numerical description of image points on a seismic section or radargram. We have inv

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27

Rignot, E., and R. Chellappa. "Maximum a posteriori classification of multifrequency, multilook, synthetic aperture radar intensity data." Journal of the Optical Society of America A 10, no. 4 (1993): 573. http://dx.doi.org/10.1364/josaa.10.000573.

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28

Li, Vincent Y. F., and Keith M. Miller. "Target Detection in Radar: Current Status and Future Possibilities." Journal of Navigation 50, no. 2 (1997): 303–13. http://dx.doi.org/10.1017/s0373463300023924.

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Most of the radar systems used in operating marine vessel traffic management services experience problems, such as track loss and track swap, which may cause confusion to the traffic regulators and lead to potential hazards in the harbour operation. The reason is mainly due to the limited adaptive capabilities of the algorithms used in the detection process. The decision on whether a target is present is usually based on the amplitude information of the returning echoes. Such method has a low efficiency in discriminating between the target and clutter, especially when the signal-to-noise ratio

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29

Nazari, Mousa, and Saeid Pashazadeh. "Real-time adaptive fuzzy density clustering for multi-target data association." Intelligent Data Analysis 25, no. 1 (2021): 5–19. http://dx.doi.org/10.3233/ida-194978.

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The problem of data association for tracking multiple targets based on using the ship-borne radar is addressed in this study. A robust fuzzy density clustering algorithm is proposed, that contains three steps. At first, a customized form of adaptive density clustering is used to determine valid measurements for each target’s state. In the second step, the degree of fuzzy membership for each valid measurement is determined based on the maximum entropy approach. At the final step, the measurements with a maximum degree of membership are used for updating the position of the targets. The proposed

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30

Manangka, Leonardo Rudolf, Herwin Suprijono, and Dedi Nurcipto. "Pengenalan Pola Lintasan Berbasis Neural Network Pada Prototype Self-Driving Car." Elektrika 12, no. 2 (2020): 67. http://dx.doi.org/10.26623/elektrika.v12i2.2732.

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<p><em>Self driving cars are an interesting topic to discuss due to the high level of traffic accidents that occur due to human error. Self driving cars are vehicles that can find out about the environment with minimal human intervention. Self driving itself has many development methods such as Light Detection and Ranging (LIDAR), cameras, radars, or a combination of these sensors. This study made a prototype self-driving car using a camera as a sensor and a neural network algorithm for pattern recognition. The pattern recognition in question is the image recognition of the path ta

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31

Frank, Lawrence R., Vitaly L. Galinsky, Leigh Orf, and Joshua Wurman. "Dynamic Multiscale Modes of Severe Storm Structure Detected in Mobile Doppler Radar Data by Entropy Field Decomposition." Journal of the Atmospheric Sciences 75, no. 3 (2018): 709–30. http://dx.doi.org/10.1175/jas-d-17-0117.1.

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Abstract The detection of complex spatially and temporally varying coherent structures in data from highly nonlinear and non-Gaussian systems is a challenging problem in a wide range of scientific disciplines. This is the case in the analysis of Doppler on Wheels (DOW) mobile Doppler radar (MDR) data where the goal is to detect rapidly evolving coherent storm structures that reflect the complex interplay of nonlinear dynamical processes. Estimating and quantifying such structures from the noisy and relatively sparsely sampled MDR data poses a difficult inverse problem for which traditional ana

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32

Lele, A. S., S. R. Kulkarni, and A. S. Willsky. "Convex-polygon estimation from support-line measurements and applications to target reconstruction from laser-radar data." Journal of the Optical Society of America A 9, no. 10 (1992): 1693. http://dx.doi.org/10.1364/josaa.9.001693.

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33

Li, Caicai, Guisheng Liao, Shengqi Zhu, and Sunyong Wu. "An ESPRIT-like algorithm for coherent DOA estimation based on data matrix decomposition in MIMO radar." Signal Processing 91, no. 8 (2011): 1803–11. http://dx.doi.org/10.1016/j.sigpro.2011.02.004.

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Bugarinović, Željko, Simone Meschino, Milan Vrtunski, et al. "Automated Data Extraction from Synthetic and Real Radargrams of Complex Structures." Journal of Environmental and Engineering Geophysics 23, no. 4 (2018): 407–21. http://dx.doi.org/10.2113/jeeg23.4.407.

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This paper presents a comparative study of two algorithms for detecting and analyzing the characteristic shapes of reflection obtained as a result of Ground-Penetrating Radar (GPR) scanning technology. The first algorithm is a sub-array processing method that uses direction-of-arrival algorithms and the matched filter technique; this approach is implemented in SPOT-GPR (release 1.0), a new freeware tool for the detection and localization of targets in radargrams. The second algorithm, APEX, is based on machine learning and pattern recognition techniques and it allows finding the coordinates of

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Korotun, V. M., S. S. Golovin, and A. L. Gavrik. "MEASUREMENT OF THE VARIATIONS OF THE PHASE OF A SIGNAL IS A DUALFREQUENCY RECEIVER RADIO NAVIGATION SYSTEM GLONASS, IN ORDER TO RECOGNIZE OBJECTS BASED ON THEIR RADAR PORTRAITS FOR MEDIA AND SYSTEM KKP." Issues of radio electronics, no. 3 (March 20, 2018): 35–39. http://dx.doi.org/10.21778/2218-5453-2018-3-35-39.

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Proposals for constructing radio images of objects based on the synthesis of apertures are considered. The methods of radar recognition of space objects (QRs) are based on the deterministic model of the reflected signal. The deterministic model of the signal allows us to reproduce, without limitations, auxiliary signs used in the detection and recognition of targets (statistical parameters and generalized functions of signal realizations, etc.). It is necessary to obtain typical values of the parameters of the received signal for each typical structural element, and also to obtain a reliable s

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36

Tanguay, Armand R. "Physical and Technological Limitations of Optical Information Processing and Computing." MRS Bulletin 13, no. 8 (1988): 36–41. http://dx.doi.org/10.1557/s0883769400064666.

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Over the past four decades, the growth of information processing and computational capacity has been truly remarkable, paced to a large extent by equally remarkable progress in the integration and ultra-miniaturization of semiconductor devices. And yet it is becoming increasingly apparent that currently envisioned electronic processors and computers are rapidly approaching technological barriers that delimit processing speed, computational sophistication, and throughput per unit dissipated power. This realization has in turn led to intensive efforts to circumvent such bottlenecks through appro

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Hollands, Thomas, and Wolfgang Dierking. "Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results." Annals of Glaciology 52, no. 57 (2011): 311–17. http://dx.doi.org/10.3189/172756411795931462.

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AbstractSea-ice drift fields were obtained from sequences of synthetic aperture radar (SAR) images using a method based on pattern recognition. the accuracy of the method was estimated for two image products of the Envisat Advanced SAR (ASAR) with 25 m and 150 m pixel size. For data from the winter season it was found that 99% of the south–north and west–east components of the determined displacement vector are within ±3–5 pixels of a manually derived reference dataset, independent of the image resolution. For an image pair with 25 m resolution acquired during summer, the corresponding value i

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Crisóstomo de Castro Filho, Hugo, Osmar Abílio de Carvalho Júnior, Osmar Luiz Ferreira de Carvalho, et al. "Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series." Remote Sensing 12, no. 16 (2020): 2655. http://dx.doi.org/10.3390/rs12162655.

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The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Sh

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Yang, Hong, Yasheng Zhang, and Wenzhe Ding. "A Fast Recognition Method for Space Targets in ISAR Images Based on Local and Global Structural Fusion Features with Lower Dimensions." International Journal of Aerospace Engineering 2020 (February 14, 2020): 1–21. http://dx.doi.org/10.1155/2020/3412582.

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Feature extraction is the key step of Inverse Synthetic Aperture Radar (ISAR) image recognition. However, limited by the cost and conditions of ISAR image acquisition, it is relatively difficult to obtain large-scale sample data, which makes it difficult to obtain target deep features with good discriminability by using the currently popular deep learning method. In this paper, a new method for low-dimensional, strongly robust, and fast space target ISAR image recognition based on local and global structural feature fusion is proposed. This method performs the trace transformation along the lo

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Flouzat, Guy, Florence Laporterie, Marie-Jose Lefevre-Fonollosa, and Erick Lopez-Ornelas. "PRESENT TRENDS IN EARTH TERRESTRIAL SURFACES OBSERVATION FROM SPACE. EXPECTED PAYLOADS, DATA COOPERATION AND IMAGE ANALYSIS." Image Analysis & Stereology 21, no. 4 (2011): 87. http://dx.doi.org/10.5566/ias.v21.ps87-s97.

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This paper presents remote sensing sensors: SPOT and LANDSAT systems are the most important providers of data in the 10-30 m spatial resolution range; NOAA (AVHRR radiometer) and SPOT4-5 (VEGETATION radiometer) provide coarse spatial resolution (-1 km) but high frequency data. Microwave sensors are more recently available (radar on board of ERSl, ERS2, and RADARSAT). The principal methods of data cooperation to get benefits of these new technologies are described. Many combinations in the field of data fusion will contribute to enhance the perception of the terrestrial surfaces. A tentative re

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Petković, Miro, Igor Vujović, and Ivica Kuzmanić. "An Overview on Horizon Detection Methods in Maritime Video Surveillance." Transactions on Maritime Science 9, no. 1 (2020): 106–12. http://dx.doi.org/10.7225/toms.v09.n01.010.

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The interest in video surveillance has been increasing in the fields of maritime industry in the past decade. Maritime transportation system is a vital part of the world’s economy and the extent of global ship traffic is increasing. This trend encourages the development of intelligent surveillance systems in the maritime zone. The development of intelligent surveillance systems includes sensor and data fusion, which incorporates multispectral and multisensory data to replace the traditional approach with radars only. Video cameras are widely used since they capture images of greater resolution

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Skuratov, Victor, Konstantin Kuzmin, Igor Nelin, and Mikhail Sedankin. "APPLICATION OF A CONVOLUTIONAL NEURAL NETWORK AND A KOHONEN NETWORK FOR ACCELERATED DETECTION AND RECOGNITION OF OBJECTS IN IMAGES." EUREKA: Physics and Engineering 4 (July 31, 2020): 11–18. http://dx.doi.org/10.21303/2461-4262.2020.001360.

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One of the most effective ways to improve the accuracy and speed of algorithms for searching and recognizing objects in images is to pre-select areas of interest in which it is likely to detect objects of interest. To determine areas of interest in a pre-processed radar or satellite image of the underlying surface, the Kohonen network was used. The found areas of interest are sent to the convolutional neural network, which provides the final detection and recognition of objects. The combination of the above methods allows to speed up the process of searching and recognizing objects in images,

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43

Sun, Zhensheng, Miao Liu, Peng Liu, et al. "SAR Image Classification Using Fully Connected Conditional Random Fields Combined with Deep Learning and Superpixel Boundary Constraint." Remote Sensing 13, no. 2 (2021): 271. http://dx.doi.org/10.3390/rs13020271.

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As one of the most important active remote sensing technologies, synthetic aperture radar (SAR) provides advanced advantages of all-day, all-weather, and strong penetration capabilities. Due to its unique electromagnetic spectrum and imaging mechanism, the dimensions of remote sensing data have been considerably expanded. Important for fundamental research in microwave remote sensing, SAR image classification has been proven to have great value in many remote sensing applications. Many widely used SAR image classification algorithms rely on the combination of hand-designed features and machine

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Adriano, Bruno, Junshi Xia, Gerald Baier, Naoto Yokoya, and Shunichi Koshimura. "Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia." Remote Sensing 11, no. 7 (2019): 886. http://dx.doi.org/10.3390/rs11070886.

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This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different co

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Silva, Clauzionor L., Norberto Morales, Alvaro P. Crósta, Solange S. Costa, and Jairo R. Jiménez-Rueda. "Analysis of tectonic-controlled fluvial morphology and sedimentary processes of the western Amazon Basin: an approach using satellite images and digital elevation model." Anais da Academia Brasileira de Ciências 79, no. 4 (2007): 693–711. http://dx.doi.org/10.1590/s0001-37652007000400010.

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An investigation of the tectonic controls of the fluvial morphology and sedimentary processes of an area located southwest of Manaus in the Amazon Basin was conducted using orbital remote sensing data. In this region, low topographic gradients represent a major obstacle for morphotectonic analysis using conventional methods. The use of remote sensing data can contribute significantly to overcome this limitation. In this instance, remote sensing data comprised digital elevation model (DEM) acquired by the Shuttle Radar Topographic Mission (SRTM) and Landsat Thematic Mapper images. Advanced imag

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Wan, Tao, Xinying Fu, Kaili Jiang, Yuan Zhao, and Bin Tang. "Radar Antenna Scan Pattern Intelligent Recognition Using Visibility Graph." IEEE Access 7 (2019): 175628–41. http://dx.doi.org/10.1109/access.2019.2957769.

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Ehret, Bernd. "Pattern recognition of geophysical data." Geoderma 160, no. 1 (2010): 111–25. http://dx.doi.org/10.1016/j.geoderma.2009.09.008.

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Kadochnikov, A., A. Kazantsev, O. Mishukov, and S. Shigorev. "Pattern Radar Images Formation’s Like a Stochastic Differential Equations for Recognition of Space Objects." Proceedings of Telecommunication Universities 5, no. 4 (2019): 106–13. http://dx.doi.org/10.31854/1813-324x-2019-5-4-106-113.

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The resource problems of the traditional use of detailed radar images for reliable recognition of space objects are shown. The urgent task of forming a new type of model of radar images of space objects to determine the signs of their recognition is posed. Corresponding mathematical models of such images based on stochastic differential equations of elliptic type are presented. The adequacy of the developed models to the real radar images of a space object was assessed. It is established that for the description of radar images of space objects the most suitable is a modified model in the form

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Yuan, Xiao, Tao Tang, De Liang Xiang, and Yi Su. "SAR Image Recognition via Local Gradient Ratio Pattern." Applied Mechanics and Materials 624 (August 2014): 344–47. http://dx.doi.org/10.4028/www.scientific.net/amm.624.344.

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Synthetic Aperture Radar recognition is a non-trivial problem. New features of SAR image are proposed. Based on the gradient ratio pattern for each pixel, the Local Gradient Ratio Pattern Histogram is then computed. Next, multi-scale LGRPH is constructed for dimensionality reduction. Finally, the similarity is obtained by utilizing K-L discrepancy to measure the distance of MLGRPH. The proposed method is theoretically proved to be insensitive to speckle noise, and the adaptability to local gradient variation is also discussed. Experimental results show that the proposed approach performs well.

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Gatts, C., and A. Mariano. "Data Categorization and Neural Pattern Recognition." Microscopy and Microanalysis 3, S2 (1997): 933–34. http://dx.doi.org/10.1017/s1431927600011557.

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The natural ability of Artificial Neural Networks to perform pattern recognition tasks makes them a valuable tool in Electron Microscopy, especially when large data sets are involved. The application of Neural Pattern Recognition to HREM, although incipient, has already produced interesting results both for one dimensional spectra and 2D images.In the case of ID spectra, e.g. a set of EELS spectra acquired during a line scan, given a “vigilance parameter” (which sets the threshold for the correlation between two spectra to be high enough to consider them as similar) an ART-like network can dis

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