Current Retrieval Methods Research Articles

An improved current retrieval method for ice-edge eddies from multi-sensor remote sensing image sequences

ABSTRACT Eddies in the Marginal Ice Zones (MIZ) contribute to an enhanced melting of sea ice by forcing contact between the sea ice and the warmer water off the ice edge. The horizontal current field can be retrieved by tracking the movement of ice floes between sequential satellite images. However, the traditional method of current retrieval imposes stringent requirements on the time intervals between sequential images, and the performance of the traditional method diminishes rapidly with increasing time intervals. In this paper, an improved method of retrieving currents from multi-sensor remote sensing images is proposed, which can significantly improve the accuracy of current retrieval. The method first extracts feature from remote sensing images based on texture features and a support vector machine model and then extracts prior information about the ice-edge eddy from the first two remote sensing images. Under the guidance of prior information, horizontal currents of the eddy are retrieved by tracking the movement of sea ice from sequential remote sensing images. The proposed method was used to retrieve currents from Sentinel-1 and Sentinel-2 sequential images over the MIZ of the Fram Strait. Comparisons with the traditional method show encouraging results that the proposed method can significantly reduce the number of mismatches and improve the reliability of current vectors.

Exploiting the Matched Filter to Improve the Detection of Methane Plumes with Sentinel-2 Data

Existing research indicates that detecting near-surface methane point sources using Sentinel-2 satellite imagery can offer crucial data support for mitigating climate change. However, current retrieval methods necessitate the identification of reference images unaffected by methane, which presents certain limitations. This study introduces the use of a matched filter, developing a novel methane detection algorithm for Sentinel-2 imagery. Compared to existing algorithms, this algorithm does not require selecting methane-free images from historical imagery in methane-sensitive bands, but estimates the background spectral information across the entire scene to extract methane gas signals. We tested the algorithm using simulated Sentinel-2 datasets. The results indicated that the newly proposed algorithm effectively reduced artifacts and noise. It was then validated in a known methane emission point source event and a controlled release experiment for its ability to quantify point source emission rates. The average estimated difference between the new algorithm and other algorithms was about 34%. Compared to the actual measured values in the controlled release experiment, the average estimated values ranged from −48% to 42% of the measurements. These estimates had a detection limit ranging from approximately 1.4 to 1.7 t/h and an average error percentage of 19%, with no instances of false positives reported. Finally, in a real case scenario, we demonstrated the algorithm’s ability to precisely locate the source position and identify, as well as quantify, methane point source emissions.

A Composite Initialization Method for Phase Retrieval

Phase retrieval is a classical inverse problem with respect to recovering a signal from a system of phaseless constraints. Many recently proposed methods for phase retrieval such as PhaseMax and gradient-descent algorithms enjoy benign theoretical guarantees on the condition that an elaborate estimate of true solution is provided. Current initialization methods do not perform well when number of measurements are low, which deteriorates the success rate of current phase retrieval methods. We propose a new initialization method that can obtain an estimate of the original signal with uniformly higher accuracy which combines the advantages of the null vector method and maximal correlation method. The constructed spectral matrix for the proposed initialization method has a simple and symmetrical form. A lower error bound is proved theoretically as well as verified numerically.

A semi-explicit short text retrieval method combining Wikipedia features

With the advantages such as openness, interactivity, immediacy, and simplicity, the large number of short text data appear in the Web information space. Considering the short length, little information, sparse features and irregular grammar, the traditional information analyzing and retrieval technologies cannot deal with short text effectively. In view of the above problems, in this paper a new short text retrieval method based on the current mainstream semantic knowledge source, Wikipedia, is proposed. To be specific, a semantic feature selection algorithm is proposed to return the top k most relevant Wikipedia concepts as the whole vector space for a given short text. Thus, by analyzing the topic information of the semantic features contained in Wikipedia concepts, we propose some formulas to determine the association coefficient list between different components of the corresponding positions in two different feature vectors. On this basis, a new semantic relatedness assessment method under this lower dimensional semantic space is designed. According to computing and sorting the semantic relatedness between user queries and the target short text, a novel semi-explicit short text retrieval method combining Wikipedia concept feature and the corresponding topic information is proposed. Lastly, based on the experimental results on twitter subsets, we verify that our proposal has advantages over other some current retrieval methods on MAP, P@k and R-Prec, and can return more valid results.

Applications of Microwave Remote Sensing of Soil Moisture for Water Resources and Agriculture

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. There has been significant progress in the application of microwave remote sensing for measuring soil moisture. Both passive and active systems have demonstrated the capability to measure soil moisture, and there have been a number of studies using aircraft and space borne data that have demonstrated its usefulness for agricultural and hydrologic applications. Microwave remote sensing of soil moisture has been an active area of research since the 1970s but has yet found little use in operational applications. However, there are still several unresolved questions regarding the optimal instrument configuration and other target characteristics such as roughness and vegetation. In addition, the most likely disciplines for using these data, agriculture and hydrology, do not currently possess adequate models or procedures to use this new technology. Given recent advances in retrieval algorithms and the approval of a dedicated soil moisture satellite, it is time to re-assess the potential of various satellite systems to provide soil moisture information for hydrologic applications in an operational fashion. . A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive microwave soil moisture sensors currently considered for space operation are in the range 10–20 km. The most useful frequency range for soil moisture sensing is 1–5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods This paper reviews recent progress made with retrieving surface soil moisture from three types of microwave sensors – radiometers, Synthetic Aperture Radars (SARs), and scatter meters.

A New Wind Speed Retrieval Method for an Ocean Surface Using the Waveform Width of a Laser Altimeter

ABSTRACTThe current retrieval method of sea wind speeds using a space-borne laser device is based on the received energies, which are significantly influenced by the absorption and scatter effect in the atmosphere. A new method is derived to retrieve the wind speed by the path of waveform width—root mean square (RMS) surface height—wind speed, which differs from the classical path of received energy—mean sea slope—wind speed. First, by deriving a more rigorous expression of the waveform model of an ocean surface for a laser altimeter, the theoretical relationship between the surface height RMS and waveform width is established. Second, the surface height RMS is fitted to a new empirical formula that is related to the wind speed using the geoscience laser altimeter system (GLAS) waveform data and the National Centers for Environmental Prediction wind speeds. Then, the JONSWAP spectra with swell effect and the waveform simulator are used to generate sea surface profiles and echo waveforms to verify the new retrieval method. Finally, in a Pacific region by using GLAS waveform data, the retrieval results indicate that the bias was approximately 0.2 m/s, the RMSE was approximately 1.2 m/s, and the ratio of valid data exceeded 20%.

Added value of far‐infrared radiometry for remote sensing of ice clouds

AbstractSeveral cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, these observations only cover the midinfrared (MIR, λ < 15 μm) part of the spectrum, and none are available in the far‐infrared (FIR, λ≥ 15 μm). Using the optimal estimation method, we show that adding a few FIR channels to existing spaceborne radiometers would significantly improve their ability to retrieve ice cloud radiative properties. For clouds encountered in the polar regions and the upper troposphere, where the atmosphere is sufficiently transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. Notably, this would extend the range of applicability of current retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection‐based algorithms would thus be reached in nighttime conditions. Since the sensitivity of ice cloud thermal emission to effective particle diameter is approximately 5 times larger in the FIR than in the MIR, using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes. This is highly relevant for cirrus clouds and convective towers. This is also essential to study precipitation in the driest regions of the atmosphere, where strong feedbacks are at play between clouds and water vapor. The deployment in the near future of a FIR spaceborne radiometer is technologically feasible and should be strongly supported.

EXPLORING BIASES OF ATMOSPHERIC RETRIEVALS IN SIMULATED JWST TRANSMISSION SPECTRA OF HOT JUPITERS

ABSTRACT With a scheduled launch in 2018 October, the James Webb Space Telescope (JWST) is expected to revolutionize the field of atmospheric characterization of exoplanets. The broad wavelength coverage and high sensitivity of its instruments will allow us to extract far more information from exoplanet spectra than what has been possible with current observations. In this paper, we investigate whether current retrieval methods will still be valid in the era of JWST, exploring common approximations used when retrieving transmission spectra of hot Jupiters. To assess biases, we use 1D photochemical models to simulate typical hot Jupiter cloud-free atmospheres and generate synthetic observations for a range of carbon-to-oxygen ratios. Then, we retrieve these spectra using TauREx, a Bayesian retrieval tool, using two methodologies: one assuming an isothermal atmosphere, and one assuming a parameterized temperature profile. Both methods assume constant-with-altitude abundances. We found that the isothermal approximation biases the retrieved parameters considerably, overestimating the abundances by about one order of magnitude. The retrieved abundances using the parameterized profile are usually within 1σ of the true state, and we found the retrieved uncertainties to be generally larger compared to the isothermal approximation. Interestingly, we found that by using the parameterized temperature profile we could place tight constraints on the temperature structure. This opens the possibility of characterizing the temperature profile of the terminator region of hot Jupiters. Lastly, we found that assuming a constant-with-altitude mixing ratio profile is a good approximation for most of the atmospheres under study.

On Shipboard Marine X-Band Radar Near-Surface Current ‘‘Calibration’’

AbstractThe ocean wave signatures within conventional noncoherent marine X-band radar (MR) image sequences can be used to derive near-surface current information. On ships, an accurate near-real-time record of the near-surface current could improve navigational safety. It could also advance understanding of air–sea interaction processes. The standard shipboard MR near-surface current estimates were found to have large errors (of the same order of magnitude as the signal) that are associated with ship speed and heading. For acoustic Doppler current profilers (ADCPs), ship heading errors are known to induce a spurious cross-track current that is proportional to the ship speed and the sine of the error angle. Conventional mechanical gyrocompasses are very reliable heading sensors, but they are too inaccurate for shipboard ADCPs. Within the ADCP community, it is common practice to correct the gyrocompass measurements with the help of multiantenna carrier-phase differential GPS systems. This study shows how a similar multiantenna GPS-based ship heading correction technique stands to improve the accuracy of MR near-surface current estimates. Changes to the standard MR near-surface current retrieval method that are necessary for high-quality results from ships are also introduced. MR and ADCP data collected from R/V Roger Revelle during the Impact of Typhoons on the Ocean in the Pacific (ITOP) program in 2010 are used to demonstrate the MR currents’ accuracy and reliability.

분포형 비차등위상차를 이용한 S-밴드 이중편파레이더의 정량적 강우 추정

One of main benefits of a dual polarization radar is improvement of quantitative rainfall estimation. In this paper, performance of two representative rainfall estimation methods for a dual polarization radar, JPOLE and CSU algorithms, have been compared by using data from a MOLIT S-band dual polarization radar. In addition, this paper presents evaluation of specific differential phase () retrieval algorithm proposed by Lim et al. (2013). Current retrieval methods are based on range filtering technique or regression analysis. However, these methods can result in underestimating peak or negative values in convective regions, and fluctuated in low rain rate regions. To resolve these problems, this study applied the distribution method suggested by Lim et al. (2013) and evaluated by adopting new to JPOLE and CSU algorithms. Data were obtained from the Mt. Biseul radar of MOLIT for two rainfall events in 2012. Results of evaluation showed improvement of the peak and did not show fluctuation and negative values. Also, in heavy rain (daily rainfall > 80 mm), accumulated daily rainfall using new was closer to AWS observation data than that using legacy , but in light rain(daily rainfall is used mostly in case of heavy rain rate of quantitative rainfall estimation algorithm.

Research on Industry Image Technology with the Integration Algorithm of Color Features and Feedback of Image Retrieval

Current image retrieval methods use only one kind of image features, which can not describe image content completely. In this paper, an image retrieval method based on color and texture integration is proposed. Calculation of similarity for the individual features is normalized before color feature integration. In order to improve the precision of image retrieval, a relevance feedback mechanism is invoked. The experiment results show that the proposed method has an excellent retrieval performance.

A Survey on Motion Capture Data Retrieval

With the rapid development of motion capture technology, large motion capture databases are established. How to effectively retrieve the motions from huge amounts of motion data has become a hot topic in computer animation. In this paper, we give a survey on current motion capture data retrieval methods and point out some still existing problems at the end.

Quranic Verse Extraction base on Concepts using OWL-DL Ontology

In recent years, there has been a global growing demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about a number of automated applications that ease the retrieval of knowledge from the Holy Book, being the major source of Knowledge in Islam. However, the current retrieval methods in the Quranic domain lack adequate semantic search capabilities; they are mostly based on the keywords matching approach. There is a lack of adequate linked data to provide a better description of concepts found in the Holy Quran. In this study we propose an Ontology assisted semantic search system in the Qur’an domain. The system makes use of Quran ontology and various relationships and restrictions. This will enable the user to semantically search for verses related to their query in Al-Quran. The system has improved the search capability of the Holy Quran knowledge to 95 percent accuracy level.

A DESIGN FOR IMPROVING THE CORE SHELF SYSTEM IN A CORE REPOSITORY

Current core repository storage strategies and exis ting problems with current core rack retrieval were analyzed. Current retrieval methods were found to be inefficient both in the st orage and retrieval of core samples. This prompted a study on improvements in storage algorithms. This new algorithm allows for improvements in rack structure, identification syst ems and databases used for core storage. This is done by utilizing structured infor mation gathering for core storage areas, core obser vation areas and transportation devices and therefore can effectively improve the w ork efficiency in reducing work intensity and conve nient data processing.

Personalized Semantic Retrieval and Summarization of Web Based Documents

The current retrieval methods are essentially based on the string-matching approach lacking of semantic information and can’t understand the user's query intent and interest very well. These methods do regard as the personalization of the users. Semantic retrieval techniques are performed by interpreting the semantic of keywords. Using the text summarization allows a user to get a sense of the content of a full-text, or to know its information content, without reading all sentences within the full-text. In this paper, a semantic personalized information retrieval (IR) system is proposed, oriented to the exploitation of Semantic Web technology and WordNet ontology to support semantic IR capabilities in Web documents. In a proposed system, the Web documents are represented in concept vector model using WordNet. Personalization is used in a proposed system by building user model (UM). Text summarization in a proposed system is based on extracting the most relevant sentences from the original document to form a summary using WordNet. The examination of the proposed system is performed by using three experiments that are based on relevance based evaluation. The results of the experiment shows that the proposed system, which is based on Semantic Web technology, can improve the accuracy and effectiveness for retrieving relevant Web documents.

Semantic Retrieval Approach for Web Documents

Because of explosive growth of resources in the internet, the information retrieval technology has become particularly important. However the current retrieval methods are essentially based on the full text matching of keywords approach lacking of semantic information and can’t understand the user's query intent very well. These methods return a large number of irrelevant information, and are unable to meet the user's request. Systems have been established so far failed to overcome fully the limitations of search based on keywords. Such systems are built from variations of classic models that represent information by keywords. Using Semantic Web is a way to increase the precision of information retrieval systems. In this paper, we propose the semantic information retrieval approach to extract the information from the web documents in certain domain (jaundice diseases) by collecting the domain relevant documents using focused crawler based on domain ontology, and using similar semantic content that is matched with a given user’s query. Semantic retrieval approach aims to discover semantically similar terms in documents and query terms using WordNet.

An evaluation of descriptors for large-scale image retrieval from sketched feature lines

We address the problem of fast, large scale sketch-based image retrieval, searching in a database of over one million images. We show that current retrieval methods do not scale well towards large databases in the context of interactively supervised search and propose two different approaches for which we objectively evaluate that they significantly outperform existing approaches. The proposed descriptors are constructed such that both the full color image and the sketch undergo exactly the same preprocessing steps. We first search for an image with similar structure, analyzing gradient orientations. Then, best matching images are clustered based on dominant color distributions, to offset the lack of color-based decision during the initial search. Overall, the query results demonstrate that the system offers intuitive access to large image databases using a user-friendly sketch-and-browse interface.

Evaluation of the Makam Scale Theory of Arel for Music Information Retrieval on Traditional Turkish Art Music

Current music information retrieval (MIR) methods are specifically tailored to the needs of western music. Therefore, it is not straightforward to apply these methods to non-western musics such as traditional Turkish art music (TTAM). Western music theory plays a crucial role in MIR studies. The divergence, however, between theory and practice in traditional Turkish art music (TTAM) results in a lack of a reliable theory of TTAM on which MIR techniques can be based. This is particularly true for theories regarding pitch scales and interval structures in TTAM. In this paper, we evaluate the most influential (yet disputable) theory of TTAM, Arel theory, by means of a makam classification task, to understand whether it can provide a basis for MIR studies on TTAM in a similar way western music theory provides a basis for MIR studies on western music. It is shown that Arel theory is overall successful when applied for modality finding in TTAM and that it can be improved if small modifications are introduced following pitch values obtained from musical practice.

Episode-Constrained Cross-Validation in Video Concept Retrieval

Whereas video tells a narrative by a composition of shots, current video retrieval methods focus mainly on single shots. In retrieval performance estimation, similar shots in a narrative may result in performance overestimation. We propose an episode-based version of cross-validation leading up to 14% classification improvement over shot-based cross-validation.

Satellite remote sensing applications for surface soil moisture monitoring: A review

Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches for soil moisture estimation from optical, thermal, passive microwave, and active microwave measurements. The physical principles and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms and key issues that have to be addressed in the near future are also discussed.