Статті в журналах: "Fast retrieval"

1

Fournier, N., P. Stammes, M. de Graaf, R. van der A, A. Piters, M. Grzegorski, and A. Kokhanovsky. "Improving cloud information over deserts from SCIAMACHY Oxygen A-band measurements." Atmospheric Chemistry and Physics 6, no. 1 (January 25, 2006): 163–72. http://dx.doi.org/10.5194/acp-6-163-2006.

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Abstract. The retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites is sensitive to light scattered by clouds. The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on the Envisat satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) is a fast and robust algorithm providing cloud information from the O2 A-band for cloud correction of ozone. FRESCO provides a consistent set of cloud products by retrieving simultaneously effective cloud fraction and cloud top pressure. The FRESCO retrieved values are compared with the SCIAMACHY Level 2 operational cloud fraction of OCRA (Optical Cloud Recognition Algorithm) but, also, with cloud information from HICRU (Heidelberg Iterative Cloud Retrieval Utilities), SACURA (SemiAnalytical CloUd Retrieval Algorithm) and the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument. The results correlate well, but FRESCO overestimates cloud fraction over deserts. Thus, to improve retrievals at these locations, the FRESCO surface albedo databases are decontaminated from the presence of desert dust aerosols. This is achieved by using the GOME Absorbing Aerosol Index. It is shown that this approach succeeds well in producing more accurate cloud information over the Sahara.

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2

Geigle, Gregor, Jonas Pfeiffer, Nils Reimers, Ivan Vulić, and Iryna Gurevych. "Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval." Transactions of the Association for Computational Linguistics 10 (2022): 503–21. http://dx.doi.org/10.1162/tacl_a_00473.

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Abstract Current state-of-the-art approaches to cross- modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross- modal retrieval, we propose a novel fine-tuning framework that turns any pretrained text-image multi-modal model into an efficient retrieval model. The framework is based on a cooperative retrieve-and-rerank approach that combines: 1) twin networks (i.e., a bi-encoder) to separately encode all items of a corpus, enabling efficient initial retrieval, and 2) a cross-encoder component for a more nuanced (i.e., smarter) ranking of the retrieved small set of items. We also propose to jointly fine- tune the two components with shared weights, yielding a more parameter-efficient model. Our experiments on a series of standard cross-modal retrieval benchmarks in monolingual, multilingual, and zero-shot setups, demonstrate improved accuracy and huge efficiency benefits over the state-of-the-art cross- encoders.1

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3

Desmons, Marine, Ping Wang, Piet Stammes, and L. Gijsbert Tilstra. "FRESCO-B: a fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2." Atmospheric Measurement Techniques 12, no. 4 (April 23, 2019): 2485–98. http://dx.doi.org/10.5194/amt-12-2485-2019.

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Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A band) algorithm is a simple, fast and robust algorithm used to retrieve cloud information in operational satellite data processing. It has been applied to GOME-1 (Global Ozone Monitoring Experiment), SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography), GOME-2 and more recently to TROPOMI (Tropospheric Monitoring Instrument). FRESCO retrieves effective cloud fraction and cloud pressure from measurements in the oxygen A band around 761 nm. In this paper, we propose a new version of the algorithm, called FRESCO-B, which is based on measurements in the oxygen B band around 687 nm. Such a method is interesting for vegetated surfaces where the surface albedo is much lower in the B band than in the A band, which limits the ground contribution to the top-of-atmosphere reflectances. In this study we first perform retrieval simulations. These show that the retrieved cloud pressures from FRESCO-B and FRESCO differ only between −10 and +10 hPa, except for high, thin clouds over vegetation where the difference is larger (about +15 to +30 hPa), with FRESCO-B yielding higher pressure. Next, inter-comparison between FRESCO-B and FRESCO retrievals over 1 month of GOME-2B data reveals that the effective cloud fractions retrieved in the O2 A and B bands are very similar (mean difference of 0.003), while the cloud pressures show a mean difference of 11.5 hPa, with FRESCO-B retrieving higher pressures than FRESCO. This agrees with the simulations and is partly due to deeper photon penetrations of the O2 B band in clouds compared to the O2 A-band photons and partly due to the surface albedo bias in FRESCO. Finally, validation with ground-based measurements shows that the FRESCO-B cloud pressure represents an altitude within the cloud boundaries for clouds that are not too far from the Lambertian reflector model, which occurs in about 50 % of the cases.

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4

Jonkheid, B. J., R. A. Roebeling, and E. van Meijgaard. "A fast SEVIRI simulator for quantifying retrieval uncertainties in the CM SAF cloud physical property algorithm." Atmospheric Chemistry and Physics 12, no. 22 (November 20, 2012): 10957–69. http://dx.doi.org/10.5194/acp-12-10957-2012.

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Abstract. The uncertainties in the cloud physical properties derived from satellite observations make it difficult to interpret model evaluation studies. In this paper, the uncertainties in the cloud water path (CWP) retrievals derived with the cloud physical properties retrieval algorithm (CPP) of the climate monitoring satellite application facility (CM SAF) are investigated. To this end, a numerical simulator of MSG-SEVIRI observations has been developed that calculates the reflectances at 0.64 and 1.63 μm for a wide range of cloud parameter values, satellite viewing geometries and surface albedos using a plane-parallel radiative transfer model. The reflectances thus obtained are used as input to CPP, and the retrieved values of CWP are compared to the original input of the simulator. Cloud parameters considered in this paper refer to e.g. sub-pixel broken clouds and the simultaneous occurrence of ice and liquid water clouds within one pixel. These configurations are not represented in the CPP algorithm and as such the associated retrieval uncertainties are potentially substantial. It is shown that the CWP retrievals are very sensitive to the assumptions made in the CPP code. The CWP retrieval errors are generally small for unbroken single-layer clouds with COT > 10, with retrieval errors of ~3% for liquid water clouds to ~10% for ice clouds. In a multi-layer cloud, when both liquid water and ice clouds are present in a pixel, the CWP retrieval errors increase dramatically; depending on the cloud, this can lead to uncertainties of 40–80%. CWP retrievals also become more uncertain when the cloud does not cover the entire pixel, leading to errors of ~50% for cloud fractions of 0.75 and even larger errors for smaller cloud fractions. Thus, the satellite retrieval of cloud physical properties of broken clouds as well as multi-layer clouds is complicated by inherent difficulties, and the proper interpretation of such retrievals requires extra care.

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5

Masiello, Guido, Carmine Serio, Sara Venafra, Laurent Poutier, and Frank-M. Göttsche. "SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval." Sensors 19, no. 7 (March 29, 2019): 1532. http://dx.doi.org/10.3390/s19071532.

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Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.

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6

Fournier, N., P. Stammes, M. de Graaf, R. van der A, A. Piters, R. Koelemeijer, and A. Kokhanovsky. "Improving cloud information over deserts from SCIAMACHY O2 A-band." Atmospheric Chemistry and Physics Discussions 5, no. 4 (August 16, 2005): 6013–39. http://dx.doi.org/10.5194/acpd-5-6013-2005.

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Abstract. The retrieval of column densities and concentration profiles of atmospheric trace gas species from satellites is sensitive to light scattered by clouds. The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on the Envisat satellite, principally designed to retrieve trace gases in the atmosphere, is also capable of detecting clouds. FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) is a fast and robust algorithm providing cloud information from the O2 A-band for cloud correction of ozone. FRESCO provides a consistent set of cloud products by retrieving simultaneously effective cloud fraction and cloud top pressure. The FRESCO retrieved values are compared with both the SCIAMACHY Level 2 operational cloud fraction of OCRA (Optical Cloud Recognition Algorithm) and cloud information deduced from the MODIS instrument. The results correlate well, but FRESCO overestimates cloud fraction over deserts. Thus, to improve retrievals at these locations, the FRESCO surface albedo databases are decontaminated from the presence of desert dust aerosols. This is achieved by using a GOME Absorbing Aerosol Index. It is shown that this approach succeeds well in producing more accurate cloud information over the Sahara.

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7

Smith, William L., Elisabeth Weisz, Stanislav V. Kireev, Daniel K. Zhou, Zhenglong Li, and Eva E. Borbas. "Dual-Regression Retrieval Algorithm for Real-Time Processing of Satellite Ultraspectral Radiances." Journal of Applied Meteorology and Climatology 51, no. 8 (August 2012): 1455–76. http://dx.doi.org/10.1175/jamc-d-11-0173.1.

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AbstractA fast physically based dual-regression (DR) method is developed to produce, in real time, accurate profile and surface- and cloud-property retrievals from satellite ultraspectral radiances observed for both clear- and cloudy-sky conditions. The DR relies on using empirical orthogonal function (EOF) regression “clear trained” and “cloud trained” retrievals of surface skin temperature, surface-emissivity EOF coefficients, carbon dioxide concentration, cloud-top altitude, effective cloud optical depth, and atmospheric temperature, moisture, and ozone profiles above the cloud and below thin or broken cloud. The cloud-trained retrieval is obtained using cloud-height-classified statistical datasets. The result is a retrieval with an accuracy that is much higher than that associated with the retrieval produced by the unclassified regression method currently used in the International Moderate Resolution Imaging Spectroradiometer/Atmospheric Infrared Sounder (MODIS/AIRS) Processing Package (IMAPP) retrieval system. The improvement results from the fact that the nonlinear dependence of spectral radiance on the atmospheric variables, which is due to cloud altitude and associated atmospheric moisture concentration variations, is minimized as a result of the cloud-height-classification process. The detailed method and results from example applications of the DR retrieval algorithm are presented. The new DR method will be used to retrieve atmospheric profiles from Aqua AIRS, MetOp Infrared Atmospheric Sounding Interferometer, and the forthcoming Joint Polar Satellite System ultraspectral radiance data.

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8

Sun, Jian Fei, Zhi Yi Qu, and Kun Yu Wang. "Fast Image Retrieval Based Weighted Color AutoCorrelogram and LSH Indexing." Applied Mechanics and Materials 667 (October 2014): 208–12. http://dx.doi.org/10.4028/www.scientific.net/amm.667.208.

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With the Rapid Development of Internet, Image Retrieval is more and more Important,For Image Retrieval, it Requires Not only a Real-Time Retrieval Speed but also the Accuracy of the Results.The Method Described in this Article: Firstly,We should Quantify the HSV Color Space of the Image Non-Uniformly,and then Extract the Weighted Color Autocorrelogram ,and Finally Build an Indexing Library Based Locality-Sensitive Hashing.The Method Successfully Solves the Problem of Dimension Disaster.With Different Scales Image Datasets，the Experimental Results Show that the Method can Retrieve Similar Images Quickly with the Suitable Parameters Selected.

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9

Papadias, D., M. Mantzourogiannis, and I. Ahmad. "Fast retrieval of similar configurations." IEEE Transactions on Multimedia 5, no. 2 (June 2003): 210–22. http://dx.doi.org/10.1109/tmm.2003.811629.

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10

Swanson, Mitchell D. "Fast progressively refined image retrieval." Journal of Electronic Imaging 7, no. 3 (July 1, 1998): 443. http://dx.doi.org/10.1117/1.482611.

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11

Henderson, Jane. "Flexible Storage and Fast Retrieval." VINE 22, no. 3 (March 1992): 16–21. http://dx.doi.org/10.1108/eb040481.

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12

Leinonen, Jussi, Matthew D. Lebsock, Simone Tanelli, Ousmane O. Sy, Brenda Dolan, Randy J. Chase, Joseph A. Finlon, Annakaisa von Lerber, and Dmitri Moisseev. "Retrieval of snowflake microphysical properties from multifrequency radar observations." Atmospheric Measurement Techniques 11, no. 10 (October 5, 2018): 5471–88. http://dx.doi.org/10.5194/amt-11-5471-2018.

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Abstract. We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX–RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of high-density snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multifrequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.

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13

Dong, Xiajun, Bin Huang, and Yuncai Zhou. "Research on Fast Face Retrieval Optimization Algorithm Based on Fuzzy Clustering." Scientific Programming 2022 (January 7, 2022): 1–7. http://dx.doi.org/10.1155/2022/6588777.

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Aiming at the problem of long retrieval time for massive face image databases under a given threshold, a fast retrieval algorithm for massive face images based on fuzzy clustering is proposed. The algorithm builds a deep convolutional neural network model. The model can be used to extract features from face photos to obtain a high-dimensional vector to represent the high-level semantic features of face photos. On this basis, the fuzzy clustering algorithm is used to perform fuzzy clustering on the feature vectors of the face database to construct a retrieval pedigree map. When the threshold is passed in for database retrieval of the target face photos, the pedigree map can be quickly retrieved. Experiments on the LFW face dataset and self-collected face dataset show that the model is better than the commonly used K-means model in face recognition accuracy, clustering effect, and retrieval speed and has certain commercial value.

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14

Wang, P., P. Stammes, R. van der A, G. Pinardi, and M. van Roozendael. "FRESCO+: an improved O2 A-band cloud retrieval algorithm for tropospheric trace gas retrievals." Atmospheric Chemistry and Physics 8, no. 21 (November 14, 2008): 6565–76. http://dx.doi.org/10.5194/acp-8-6565-2008.

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Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm has been used to retrieve cloud information from measurements of the O2 A-band around 760 nm by GOME, SCIAMACHY and GOME-2. The cloud parameters retrieved by FRESCO are the effective cloud fraction and cloud pressure, which are used for cloud correction in the retrieval of trace gases like O3 and NO2. To improve the cloud pressure retrieval for partly cloudy scenes, single Rayleigh scattering has been included in an improved version of the algorithm, called FRESCO+. We compared FRESCO+ and FRESCO effective cloud fractions and cloud pressures using simulated spectra and one month of GOME measured spectra. As expected, FRESCO+ gives more reliable cloud pressures over partly cloudy pixels. Simulations and comparisons with ground-based radar/lidar measurements of clouds show that the FRESCO+ cloud pressure is about the optical midlevel of the cloud. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher than the FRESCO cloud pressure, while the FRESCO+ effective cloud fraction is about 0.01 larger. The effect of FRESCO+ cloud parameters on O3 and NO2 vertical column density (VCD) retrievals is studied using SCIAMACHY data and ground-based DOAS measurements. We find that the FRESCO+ algorithm has a significant effect on tropospheric NO2 retrievals but a minor effect on total O3 retrievals. The retrieved SCIAMACHY tropospheric NO2 VCDs using FRESCO+ cloud parameters (v1.1) are lower than the tropospheric NO2VCDs which used FRESCO cloud parameters (v1.04), in particular over heavily polluted areas with low clouds. The difference between SCIAMACHY tropospheric NO2 VCDs v1.1 and ground-based MAXDOAS measurements performed in Cabauw, The Netherlands, during the DANDELIONS campaign is about −2.12×1014molec cm−2.

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15

Gao, Meng, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, et al. "Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean." Atmospheric Measurement Techniques 15, no. 16 (August 25, 2022): 4859–79. http://dx.doi.org/10.5194/amt-15-4859-2022.

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Abstract. Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

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16

Somkuti, Peter, Hartmut Bösch, and Robert J. Parker. "The Significance of Fast Radiative Transfer for Hyperspectral SWIR XCO2 Retrievals." Atmosphere 11, no. 11 (November 12, 2020): 1219. http://dx.doi.org/10.3390/atmos11111219.

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Fast radiative transfer (RT) methods are commonplace in most algorithms which retrieve the column-averaged dry-mole fraction of carbon dioxide (XCO2) in the Earth’s atmosphere. These methods are required to keep the computational effort at a manageable level and to allow for operational processing of tens of thousands of measurements per day. Without utilizing any fast RT method, the involved computation times would be one to two orders of magnitude larger. In this study, we investigate three established methods within the same retrieval algorithm, and for the first time, analyze the impact of the fast RT method while keeping every other aspect of the algorithm the same. We perform XCO2 retrievals on measurements from the OCO-2 instrument and apply quality filters and parametric bias correction. We find that the central 50% of scene-by-scene differences in XCO2 between retrieval sets, after threshold filtering and bias correction, that use different fast RT methods, are less than 0.40 ppm for land scenes, and less than 0.11 ppm for ocean scenes. Significant regional differences larger than 0.3 ppm are observed and further studies with larger samples and regional-scale subsets need to be undertaken to fully understand the impact on applications that utilize space-based XCO2.

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17

Bhattacharya, Sourangshu, Chiranjib Bhattacharyya, and Nagasuma R. Chandra. "Projections for fast protein structure retrieval." BMC Bioinformatics 7, Suppl 5 (2006): S5. http://dx.doi.org/10.1186/1471-2105-7-s5-s5.

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18

Super, Boaz J. "Fast Retrieval of Isolated Visual Shapes." Computer Vision and Image Understanding 85, no. 1 (January 2002): 1–21. http://dx.doi.org/10.1006/cviu.2002.0959.

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19

Gao, Yuan, Miaojing Shi, Dacheng Tao, and Chao Xu. "Database Saliency for Fast Image Retrieval." IEEE Transactions on Multimedia 17, no. 3 (March 2015): 359–69. http://dx.doi.org/10.1109/tmm.2015.2389616.

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20

Wang, P., P. Stammes, R. van der A, G. Pinardi, and M. van Roozendael. "FRESCO+: an improved O2 A-band cloud retrieval algorithm for tropospheric trace gas retrievals." Atmospheric Chemistry and Physics Discussions 8, no. 3 (May 27, 2008): 9697–729. http://dx.doi.org/10.5194/acpd-8-9697-2008.

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Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm has been used to retrieve cloud information from measurements of the O2 A-band around 760 nm by GOME, SCIAMACHY and GOME-2. The cloud parameters retrieved by FRESCO are the effective cloud fraction and cloud pressure, which are used for cloud correction in the retrieval of trace gases like O3 and NO2. To improve the cloud pressure retrieval for partly cloudy scenes, single Rayleigh scattering has been included in an improved version of the algorithm, called FRESCO+. We compared FRESCO+ and FRESCO effective cloud fractions and cloud pressures using simulated spectra and one month of GOME measured spectra. As expected, FRESCO+ gives more reliable cloud pressures over partly cloudy pixels. Simulations and comparisons with ground-based radar/lidar measurements of clouds shows that the FRESCO+ cloud pressure is about the optical midlevel of the cloud. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher than the FRESCO cloud pressure, while the FRESCO+ effective cloud fraction is about 0.01 larger. The effect of FRESCO+ cloud parameters on O3 and NO2 vertical column densities (VCD) is studied using SCIAMACHY data and ground-based DOAS measurements. We find that the FRESCO+ algorithm has a significant effect on tropospheric NO2 retrievals but a minor effect on total O3 retrievals. The retrieved SCIAMACHY tropospheric NO2 VCDs using FRESCO+ cloud parameters (v1.1) are lower than the tropospheric NO2 VCDs which used FRESCO cloud parameters (v1.04), in particular over heavily polluted areas with low clouds. The difference between SCIAMACHY tropospheric NO2 VCDs v1.1 and ground-based MAXDOAS measurements performed in Cabauw, The Netherlands, during the DANDELIONS campaign is about −2.12×1014 molec cm−2.

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21

Gong, J., and D. L. Wu. "CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances." Atmospheric Measurement Techniques 7, no. 6 (June 26, 2014): 1873–90. http://dx.doi.org/10.5194/amt-7-1873-2014.

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Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables in determining cloud radiative and thermodynamical properties in climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of the Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the empirical forward models between collocated and coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up table (LUT) of Tcir–IWP relationships as a function of ht and the frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of the normalized probability density function (PDF). Compared to the empirical model, current operational radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

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22

Gong, J., and D. L. Wu. "CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances." Atmospheric Measurement Techniques Discussions 6, no. 5 (September 4, 2013): 8187–233. http://dx.doi.org/10.5194/amtd-6-8187-2013.

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Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables to determine cloud radiative and thermodynamical properties in the climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the forward models between collocated-and-coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up-table (LUT) of Tcir–IWP relationships as a function of ht and frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of normalized probability density function (PDF). Compared to the empirical model, current radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains as an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

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23

Castelli, Elisa, Enzo Papandrea, Alessio Di Roma, Ilaria Bloise, Mattia Varile, Hamid Tabani, Jean-Philippe Gastellu-Etchegorry, and Lorenzo Feruglio. "Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations." Remote Sensing 13, no. 24 (December 9, 2021): 5003. http://dx.doi.org/10.3390/rs13245003.

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In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.

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Smith, N., H. L. Huang, E. Weisz, H. J. Annegarn, and R. B. Pierce. "High-resolution air quality monitoring from space: a fast retrieval scheme for CO from hyperspectral infrared measurements." Atmospheric Measurement Techniques Discussions 4, no. 3 (June 16, 2011): 3787–803. http://dx.doi.org/10.5194/amtd-4-3787-2011.

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Abstract. The first results of the Fast Linear Inversion Trace gas System (FLITS) retrieval scheme are presented here for CO from IASI (Infrared Atmospheric Sounding Interferometer) measurements using RAQMS (Real time Air Quality Modelling System) as atmospheric background. FLITS is a simple linear inversion scheme with a stable performance that retrieves total column CO concentrations (molec cm−2) at single field-of-view (FOV) irrespective of cloud cover. A case study is presented here for a biomass burning plume over the Pacific on 29 March 2010. For each FOV a single tropospheric CO density, vertically integrated over 200–800 hPa, is retrieved with 12 channels in the spectral range 2050–2225 cm−1. Despite variations in cloud cover and temperature, the degrees of freedom for signal (DFS) of the solution ranges between 0.8 and 0.95. In addition, the retrieval error is at least half the background error of 10 %, with dominant contribution from uncertainty in the measurement and temperature. With its stability and processing speed, FLITS meet two of the key requirements for operational processing. We conclude that the linear combination of space-borne measurements with a chemical transport model in the FLITS retrieval scheme holds potential for real-time air quality monitoring and evaluation of pollutant transport at high spatial resolution.

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Ungermann, J. "Improving retrieval quality for airborne limb-sounders by horizontal regularisation." Atmospheric Measurement Techniques Discussions 5, no. 5 (September 13, 2012): 6577–626. http://dx.doi.org/10.5194/amtd-5-6577-2012.

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Abstract. Modern airborne infrared limb-sounders are capable of measuring profiles so fast that neighbouring profiles are very similar to one another. This can be exploited by retrieving whole 2-D cross-sections instead of simple 1-D profiles. By adding horizontal regularisation in addition to a potentially reduced vertical regularisation, vertical structures can be better retrieved while maintaining or reducing the general noise level. This paper presents algorithms that are able to perform such a retrieval and efficiently produce typical diagnostic quantities. The characteristics of produced retrieval results for a variety of parametrisations is discussed in a case study that analyses a cross-section measured by the CRISTA-NF instrument during the RECONCILE campaign between Spitsbergen and Kiruna, Sweden, in March 2010. It is shown that cross-section retrievals can either reduce noise or produce finer vertical structures while maintaining the same noise level. The presented methodology can also be applied in a straightforward way to improve the retrievals for both near-future satellite-borne limb-sounders and current air- and satellite-borne nadir sounder.

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Wang, Chun Ping. "Data Mining Based Intelligent Retrieval Algorithm and its Application." Applied Mechanics and Materials 742 (March 2015): 340–43. http://dx.doi.org/10.4028/www.scientific.net/amm.742.340.

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Mathematical model of information retrieval algorithm retrieves digital libraries involved, it is very important to design an algorithm to make the best of the books, in order to extract the required information, including the association rules and classification method for from the database predicting the reader and potential use of fast and accurate information. In this paper, the intelligent data retrieval books mining algorithms to analyze, you can study the books of intelligent retrieval application, until the actual retrieval algorithm to solve the model first developed to meet the requirements, design a library of books intelligent information retrieval system .

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Xu, Man. "Deep Learning Models for Fast Retrieval and Extraction of French Speech Vocabulary Applications." Computational Intelligence and Neuroscience 2022 (July 8, 2022): 1–8. http://dx.doi.org/10.1155/2022/4286659.

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Due to the large French vocabulary, how quickly retrieve and accurately identify the required vocabulary is still a big challenge in French learning. In view of the above problems, we introduce a deep learning algorithm in this study to upgrade and optimize the retrieval system of French words and optimize the acquisition speed of speech words data and the recognition accuracy of speech words, so as to meet the needs of users for word retrieval. The results show that the two training methods of SGD synchronous update network and alternate update network parameters for fast retrieval and extraction of French speech vocabulary reduce from a maximum of 11.65% to 4.25% in the WER criterion, with a maximum reduction of 7.4%; the two training methods of SGD synchronous update network and alternate update network parameters for fast retrieval and extraction of French speech vocabulary reduce from a maximum of 13.52% to 4.4% in the SER criterion. The training methods of fast retrieval and extraction of the SGD synchronous update network and alternate update network parameters in French speech vocabulary reduced from the highest 582 ms to 351 ms in the response time criterion, with a maximum reduction of 8.84%; the maximum reduction of 39.7%. In French speech vocabulary, SGD synchronous updating network and alternating updating network parameter algorithm are used to quickly retrieve and extract French words. When the number of iterations reaches 120, the model fitting accuracy of the training set reaches 90.05%, while the model can reach 94.5% in the test set. The system has a stronger generalization ability and a higher speech vocabulary recognition rate to meet the practical requirements.

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Barret, Brice, Emanuele Emili, and Eric Le Flochmoen. "A tropopause-related climatological a priori profile for IASI-SOFRID ozone retrievals: improvements and validation." Atmospheric Measurement Techniques 13, no. 10 (October 6, 2020): 5237–57. http://dx.doi.org/10.5194/amt-13-5237-2020.

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Abstract. The MetOp/Infrared Atmospheric Sounding Interferometer (IASI) instruments have provided data for operational meteorology and document atmospheric composition since 2007. IASI ozone (O3) data have been used extensively to characterize the seasonal and interannual variabilities and the evolution of tropospheric O3 at the global scale. SOftware for a Fast Retrieval of IASI Data (SOFRID) is a fast retrieval algorithm that provides IASI O3 profiles for the whole IASI period. Until now, SOFRID O3 retrievals (v1.5 and v1.6) were performed with a single a priori profile, which resulted in important biases and probably a too-low variability. For the first time, we have implemented a comprehensive dynamical a priori profile for spaceborne O3 retrievals which takes the pixel location, time and tropopause height into account for SOFRID-O3 v3.5 retrievals. In the present study, we validate SOFRID-O3 v1.6 and v3.5 with electrochemical concentration cell (ECC) ozonesonde profiles from the global World Ozone and Ultraviolet Radiation Data Centre (WOUDC) database for the 2008–2017 period. Our validation is based on a thorough statistical analysis using Taylor diagrams. Furthermore, we compare our retrievals with ozonesonde profiles both smoothed by the IASI averaging kernels and raw. This methodology is essential to evaluate the inherent usefulness of the retrievals to assess O3 variability and trends. The use of a dynamical a priori profile largely improves the retrievals concerning two main aspects: (i) it corrects high biases for low-tropospheric O3 regions such as the Southern Hemisphere, and (ii) it increases the retrieved O3 variability, leading to a better agreement with ozonesonde data. Concerning upper troposphere–lower stratosphere (UTLS) and stratospheric O3, the improvements are less important and the biases are very similar for both versions. The SOFRID tropospheric ozone columns (TOCs) display no significant drifts (<2.5 %) for the Northern Hemisphere and significant negative ones (9.5 % for v1.6 and 4.3 % for v3.5) for the Southern Hemisphere. We have compared our validation results to those of the Fast Optimal Retrievals on Layers for IASI (FORLI) retrieval software from the literature for smoothed ozonesonde data only. This comparison highlights three main differences: (i) FORLI retrievals contain more theoretical information about tropospheric O3 than SOFRID; (ii) root mean square differences (RMSDs) are smaller and correlation coefficients are higher for SOFRID than for FORLI; (iii) in the Northern Hemisphere, the 2010 jump detected in FORLI TOCs is not present in SOFRID.

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Picchiani, M., M. Chini, S. Corradini, L. Merucci, P. Sellitto, F. Del Frate, and S. Stramondo. "Volcanic ash detection and retrievals from MODIS data by means of Neural Networks." Atmospheric Measurement Techniques Discussions 4, no. 3 (May 4, 2011): 2567–98. http://dx.doi.org/10.5194/amtd-4-2567-2011.

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Abstract. Volcanic ash clouds detection and retrieval represent a key issue for the aviation safety due to the harming effects they can provoke on aircrafts. A lesson learned from the recent Icelandic Eyjafjalla volcano eruption is the need to obtain accurate and reliable retrievals on a real time basis. The current most widely adopted procedures for ash detection and retrieval are based on the Brightness Temperature Difference (BTD) inversion observed at 11 and 12 μm that allows volcanic and meteo clouds discrimination. While ash cloud detection can be readily obtained, a reliable quantitative ash cloud retrieval can be so time consuming to prevent its utilization during the crisis phase. In this work a fast and accurate Neural Network (NN) approach to detect and retrieve volcanic ash cloud properties has been developed using multispectral IR measurements collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) over Mt. Etna volcano during 2001, 2002 and 2006 eruptive events. The procedure consists in two separate steps: the ash detection and ash mass retrieval. The detection is reduced to a classification problem by identifying two classes of "ashy" and "non-ashy" pixels in the MODIS images. Then the ash mass is estimated by means of the NN, replicating the BTD-based model performances. The results obtained from the entire procedure are very encouraging; indeed the confusion matrix for the test set has an accuracy greater than 90 %. Both ash detection and retrieval show a good agreement when compared to the results achieved by the BTD-based procedure. Moreover, the NN procedure is so fast to be extremely attractive in all the cases when the quick response time of the system is a mandatory requirement.

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Qiao, Sai. "Simulation of Fast Retrieval Method for Large-Scale Image Database." Applied Mechanics and Materials 556-562 (May 2014): 4959–62. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4959.

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The traditional database information retrieval method is achieved by retrieving simple corresponding association of the attributes, which has the necessary requirement that image only have a single characteristic, with increasing complexity of image, it is difficult to process further feature extraction for the image, resulting in great increase of time consumed by large-scale image database retrieval. A fast retrieval method for large-scale image databases is proposed. Texture features are extracted in the database to support retrieval in database. Constraints matching method is introduced, in large-scale image database, referring to the texture features of image in the database to complete the target retrieval. The experimental results show that the proposed algorithm applied in the large-scale image database retrieval, augments retrieval speed, thereby improves the performance of large-scale image database.

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Dammers, Enrico, Mark W. Shephard, Mathias Palm, Karen Cady-Pereira, Shannon Capps, Erik Lutsch, Kim Strong, et al. "Validation of the CrIS fast physical NH3 retrieval with ground-based FTIR." Atmospheric Measurement Techniques 10, no. 7 (July 25, 2017): 2645–67. http://dx.doi.org/10.5194/amt-10-2645-2017.

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Abstract. Presented here is the validation of the CrIS (Cross-track Infrared Sounder) fast physical NH3 retrieval (CFPR) column and profile measurements using ground-based Fourier transform infrared (FTIR) observations. We use the total columns and profiles from seven FTIR sites in the Network for the Detection of Atmospheric Composition Change (NDACC) to validate the satellite data products. The overall FTIR and CrIS total columns have a positive correlation of r = 0.77 (N = 218) with very little bias (a slope of 1.02). Binning the comparisons by total column amounts, for concentrations larger than 1.0 × 1016 molecules cm−2, i.e. ranging from moderate to polluted conditions, the relative difference is on average ∼ 0–5 % with a standard deviation of 25–50 %, which is comparable to the estimated retrieval uncertainties in both CrIS and the FTIR. For the smallest total column range (< 1.0 × 1016 molecules cm−2) where there are a large number of observations at or near the CrIS noise level (detection limit) the absolute differences between CrIS and the FTIR total columns show a slight positive column bias. The CrIS and FTIR profile comparison differences are mostly within the range of the single-level retrieved profile values from estimated retrieval uncertainties, showing average differences in the range of ∼ 20 to 40 %. The CrIS retrievals typically show good vertical sensitivity down into the boundary layer which typically peaks at ∼ 850 hPa (∼ 1.5 km). At this level the median absolute difference is 0.87 (std = ±0.08) ppb, corresponding to a median relative difference of 39 % (std = ±2 %). Most of the absolute and relative profile comparison differences are in the range of the estimated retrieval uncertainties. At the surface, where CrIS typically has lower sensitivity, it tends to overestimate in low-concentration conditions and underestimate in higher atmospheric concentration conditions.

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Abdul-Kareem Abdul-Azeez, Bushra. "Fast Image Retrieval Prototype using Color Descriptor." Journal of Education College Wasit University 1, no. 22 (January 18, 2016): 745–58. http://dx.doi.org/10.31185/eduj.vol1.iss22.233.

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In recent years, image retrieval prototypes become important and increased noticeably. Color feature is one of the most significant features to represent image. In this paper, we use a Dominant Color (DC) feature to represent images where each image represented by 8-DCs as maximum. Based on DCs values, image database is indexed using 3-D RGB partitioning color space. This is to reduce searching process where once a query image is given to the prototype; it will not search the whole database. Proposed technique will identify the partition and search the image within this partition only. According to the proposed method, extensive experiments were conducted on Corel databases. As a result, the retrieval time is reduced significantly without degradation to precision of retrieval.

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Nagayama, Itaru. "Fast Image Retrieval for Digital Document Processing." IEEJ Transactions on Industry Applications 128, no. 4 (2008): 543–49. http://dx.doi.org/10.1541/ieejias.128.543.

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34

Ooi, Beng Chin, Kian-Lee Tan, Tat Seng Chua, and Wynne Hsu. "Fast image retrieval using color-spatial information." VLDB Journal The International Journal on Very Large Data Bases 7, no. 2 (May 1, 1998): 115–28. http://dx.doi.org/10.1007/s007780050057.

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35

Super, Boaz J. "Fast correspondence-based system for shape retrieval." Pattern Recognition Letters 25, no. 2 (January 2004): 217–25. http://dx.doi.org/10.1016/j.patrec.2003.10.003.

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Wei, Jian, Yue Wang, Feng Liu, Qiuli Lin, and Ning Wang. "Colour fast-match for precise vehicle retrieval." Journal of Engineering 2020, no. 4 (April 1, 2020): 132–39. http://dx.doi.org/10.1049/joe.2019.0882.

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Iwen, Mark A., Aditya Viswanathan, and Yang Wang. "Fast Phase Retrieval from Local Correlation Measurements." SIAM Journal on Imaging Sciences 9, no. 4 (January 2016): 1655–88. http://dx.doi.org/10.1137/15m1053761.

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38

Yeh, Mei-Chen, and Kwang-Ting Cheng. "Fast Visual Retrieval Using Accelerated Sequence Matching." IEEE Transactions on Multimedia 13, no. 2 (April 2011): 320–29. http://dx.doi.org/10.1109/tmm.2010.2094999.

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Zhong, Zhiyuan, Jianke Zhu, and Steven C. H. Hoi. "Fast Object Retrieval Using Direct Spatial Matching." IEEE Transactions on Multimedia 17, no. 8 (August 2015): 1391–97. http://dx.doi.org/10.1109/tmm.2015.2446201.

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Sambo, F., B. Di Camillo, G. Toffolo, and C. Cobelli. "Compression and fast retrieval of SNP data." Bioinformatics 30, no. 21 (July 26, 2014): 3078–85. http://dx.doi.org/10.1093/bioinformatics/btu495.

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Liu, Haomiao, Ruiping Wang, Shiguang Shan, and Xilin Chen. "Deep Supervised Hashing for Fast Image Retrieval." International Journal of Computer Vision 127, no. 9 (March 16, 2019): 1217–34. http://dx.doi.org/10.1007/s11263-019-01174-4.

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Zhou, Daniel K., William L. Smith, Xu Liu, Allen M. Larar, Stephen A. Mango, and Hung-Lung Huang. "Physically Retrieving Cloud and Thermodynamic Parameters from Ultraspectral IR Measurements." Journal of the Atmospheric Sciences 64, no. 3 (March 1, 2007): 969–82. http://dx.doi.org/10.1175/jas3877.1.

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Abstract A physical inversion scheme has been developed dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1D) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression retrieval. The solution is iterated in order to account for nonlinearity in the 1D variational solution. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud-top level are obtained. For both optically thin and thick cloud situations, the cloud-top height can be retrieved with relatively high accuracy (i.e., error <1 km). National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed Interferometer (NAST-I) retrievals from the The Observing-System Research and Predictability Experiment (THORPEX) Atlantic Regional Campaign are compared with coincident observations obtained from dropsondes and the nadir-pointing cloud physics lidar (CPL). This work was motivated by the need to obtain solutions for atmospheric soundings from infrared radiances observed for every individual field of view, regardless of cloud cover, from future ultraspectral geostationary satellite sounding instruments, such as the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS). However, this retrieval approach can also be applied to the ultraspectral sounding instruments to fly on polar satellites, such as the Infrared Atmospheric Sounding Interferometer (IASI) on the European MetOp satellite, the Cross-track Infrared Sounder (CrIS) on the NPOESS Preparatory Project, and the follow-on NPOESS series of satellites.

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Zhang, Ming, Hai Wei Mu, Xiang Lou Liu, and Dong Yan Zhao. "An Express Retrieval Method of Face Based on Semantic Features." Advanced Materials Research 850-851 (December 2013): 905–8. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.905.

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The paper uses digital image processing technology, technology of face pattern recognition and traditional database retrieval technology, integrate image retrieval technology based on version and content, and avoid the complexity of matching image process. The experiments with 200 human samples, correctly retrieved for 155 people, exactly matched to 125. The recognition rate of the system is 75.55%, the average time of search is less than 0.1s. Experiments indicate this method has strong robustness. The semantic face image retrieval system using this method has the characteristics of fast, efficient, practical.

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Kaur, Inderpreet, Patrick Eriksson, Vasileios Barlakas, Simon Pfreundschuh, and Stuart Fox. "Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals." Remote Sensing 14, no. 7 (March 26, 2022): 1594. http://dx.doi.org/10.3390/rs14071594.

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The accurate simulation of microwave observations of clouds and precipitation are computationally challenging. A common simplification is the assumption of totally random orientation (TRO); however, studies have revealed that TRO occurs relatively infrequently in reality. A more appropriate assumption is that of azimuthally random orientation (ARO), but so far it has been a computationally expensive task. Recently a fast approximate approach was introduced that incorporates hydrometeor orientation into the assimilation of data from microwave conically scanning instruments. The approach scales the extinction in vertical (V) and horizontal (H) polarised channels to approximate ARO. In this study, the application of the approach was extended to a more basic radiative transfer perspective using the Atmospheric Radiative Transfer Simulator and the high-frequency channels of the Global Precipitation Measurement Microwave Imager (GMI). The comparison of forward simulations and GMI observations showed that with a random selection of scaling factors from a uniform distribution between 1 and 1.4–1.5, it is possible to mimic the full distribution of observed polarisation differences at 166 GHz over land and water. The applicability of this model at 660 GHz was also successfully demonstrated by means of existing airborne data. As a complement, a statistical model for polarised snow emissivity between 160 and 190 GHz was also developed. Combining the two models made it possible to reproduce the polarisation signals that were observed over all surface types, including snow and sea ice. Further, we also investigated the impact of orientation on the ice water path (IWP) retrievals. It has been shown that ignoring hydrometeor orientation has a significant negative impact (∼20% in the tropics) on retrieval accuracy. The retrieval with GMI observations produced highly realistic IWP distributions. A significant highlight was the retrieval over snow covered regions, which have been neglected in previous retrieval studies. These results provide increased confidence in the performance of passive microwave observation simulations and mark an essential step towards developing the retrievals of ice hydrometeor properties based on data from GMI, the Ice Cloud Imager (ICI) and other conically scanning instruments.

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Juan, Liu, and Xin Zheng. "A Fast Texture Recognition Technology Based on DFT." Applied Mechanics and Materials 631-632 (September 2014): 399–402. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.399.

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We adopt a fast image texture recognition technology to identify whether an image for texture image, Then we extract the texture feature for image texture, and to extract the color features for Non-texture images, By classifying different types of image retrieval to improve retrieval efficiency. The experimental results show that, this method of the rapid texture recognition technology can greatly improve the accuracy of image retrieval, and it has a great effect in terms of speed.

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Katsev, I. L., A. S. Prikhach, E. P. Zege, J. O. Grudo, and A. A. Kokhanovsky. "Speeding up the AOT retrieval procedure using RTT analytical solutions: FAR code." Atmospheric Measurement Techniques Discussions 3, no. 2 (April 12, 2010): 1645–705. http://dx.doi.org/10.5194/amtd-3-1645-2010.

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Abstract. We present here the aerosol retrieval technique that uses radiative transfer computations in the process of retrieval rather than look-up tables (LUT). This approach provides operational satellite data processing due to the use of the accurate and extremely fast radiative transfer code RAY previously developed by authors along with approximate analytical solutions of the radiative transfer theory. The aerosol optical thickness (AOT) and Angström exponent are optimized in the iteration process using the least-squares technique with fast computations of the derivatives of radiative characteristics in respect to retrieved values. The developed technique can be adapted for processing data of various satellite instruments (including any spectral multi-angle polarization-sensitive sensors). Beside, two important problems that determine the accuracy of the AOT retrieval are considered. The first one is the effect of the preliminary choice of the aerosol model, particularly for retrieval from satellite instrument providing only spectral data (MERIS, MODIS). The second problem is the influence of clouds in adjacent pixels. As for our knowledge, this problem has not been given required attention up to now and it should be properly accounted in the AOT retrieval algorithms.

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Wang, Ziyang, Wei Zheng, and Youguang Chen. "Deep learning for fast bronze inscription image retrieval." Journal of Chinese Writing Systems 4, no. 4 (December 2020): 291–96. http://dx.doi.org/10.1177/2513850220964956.

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Collections of bronze inscription images are increasing rapidly. To use these images efficiently, we proposed an effective content-based image retrieval framework using deep learning. Specifically, we extract discriminative local features for image retrieval using the activations of the convolutional neural network and binarize the extracted features for improving the efficiency of image retrieval, firstly. Then, we use the cosine metric and Euclidean metric to calculate the similarity between the query image and dataset images. The result shows that the proposed framework has an impressive accuracy.

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Shishibori, Masami, Daichi Koizumi, and Kenji Kita. "Fast Retrieval Algorithm for Earth Mover's Distance Using EMD Lower Bounds and a Skipping Algorithm." Advances in Multimedia 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/421820.

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The earth mover's distance (EMD) is a measure of the distance between two distributions, and it has been widely used in multimedia information retrieval systems, in particular, in content-based image retrieval systems. When the EMD is applied to image problems based on color or texture, the EMD reflects the human perceptual similarities. However, its computations are too expensive to use in large-scale databases. In order to achieve efficient computation of the EMD during query processing, we have developed “fastEMD,” a library for high-speed feature-based similarity retrievals in large databases. This paper introduces techniques that are used in the implementation of the fastEMD and performs extensive experiments to demonstrate its efficiency.

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WANG, XING-YUAN, and ZHI-FENG CHEN. "A FAST FRACTAL CODING IN APPLICATION OF IMAGE RETRIEVAL." Fractals 17, no. 04 (December 2009): 441–50. http://dx.doi.org/10.1142/s0218348x09004557.

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Aiming at content-based image retrieval (CBIR) in fractal domain, this paper puts forward a fast fractal encoding method to extract image features, which is based on a novel non-searching and adaptive quadtree division. As a result, it enhances fractal coding speed sharply, only needs 0.0485 seconds on average for a 256 × 256 image and is approximately 70 times faster than algorithm in addition to good reconstructed image quality. Furthermore, this paper improves image matching algorithm, consequently enhancing the accuracy of query results. In addition, we present a method to further accelerate image retrieval based on the analysis to fractal codes distance and number. Experimental results show that our proposed method is performs highly in retrieval speed and feasible in retrieval accuracy.

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Jing, Hui, Mei Fa Huang, and Cong Li. "3D Mechanical Models Retrieval Based on Combined Histograms for Rapid Product Design." Applied Mechanics and Materials 16-19 (October 2009): 65–69. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.65.

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Shape Distribution (3DSD) and Radius Angle Histogram (RAH) are useful methods for retrieving 3D model in mechanical engineering. Through these methods have advantages such as fast speeds and simple operations, the retrieval precision are not very high enough. To improve the retrieval precision, a new method named combined histograms which integrates the advantages of 3DSD and RAH is proposed. This method makes use of the information both of shape and surface of the models to be retrieved. In the retrieval process, the shape histogram and the radius angle histogram of the retrieved model are first extracted. Then, the combined histograms of the model are established by integrating the shape histogram and the radius angle histogram. To validate the proposed method, an experiment is given. The experiment results show that the proposed method has higher retrieval precision than that of 3DSD and RAH and is suitable for mechanical model design.

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