Dimension Reduction Using Quantum Wavelet Transform on a High-Performance Reconfigurable Computer

There is an increasing demand for satellite remote sensing data with both high spatial and temporal resolution in many applications. But it still is a challenge to simultaneously improve spatial resolution and temporal frequency due to the technical limits of current satellite observation systems. To this end, much R&D efforts have been ongoing for years and lead to some successes roughly in two aspects, one includes super resolution, pan-sharpen etc. methods which can effectively enhance the spatial resolution and generate good visual effects, but hardly preserve spectral signatures and result in inadequate analytical value, on the other hand, time interpolation is a straight forward method to increase temporal frequency, however it increase little informative contents in fact. In this paper we presented a novel method to simulate high resolution time series data by combing low resolution time series data and a very small number of high resolution data only. Our method starts with a pair of high and low resolution data set, and then a spatial registration is done by introducing LDA model to map high and low resolution pixels correspondingly. Afterwards, temporal change information is captured through a comparison of low resolution time series data, and then projected onto the high resolution data plane and assigned to each high resolution pixel according to the predefined temporal change patterns of each type of ground objects. Finally the simulated high resolution data is generated. A preliminary experiment shows that our method can simulate a high resolution data with a reasonable accuracy. The contribution of our method is to enable timely monitoring of temporal changes through analysis of time sequence of low resolution images only, and usage of costly high resolution data can be reduces as much as possible, and it presents a highly effective way to build up an economically operational monitoring solution for agriculture, forest, land use investigation, environment and etc. applications.

Abstract. Nowadays, Terrestrial Laser Scanning (TLS) technology is gaining popularity in monitoring and predicting the movement of landslide due to the capability of high-speed data capture without requiring a direct contact with the monitored surface. It offers very high density of point cloud data in high resolution and also can be an effective tool in detecting the surface movement of the landslide area. The aim of this research is to determine the optimal level of scanning resolution for landslide monitoring using TLS. The Topcon Geodetic Laser Scanner (GLS) 2000 was used in this research to obtain the three dimensional (3D) point cloud data of the landslide area. Four types of resolution were used during scanning operation which were consist of very high, high, medium and low resolutions. After done with the data collection, the point clouds datasets were undergone the process of registration and filtering using ScanMaster software. After that, the registered point clouds datasets were analyzed using CloudCompare software. Based on the results obtained, the accuracy of TLS point cloud data between picking point manually and computed automatically by ScanMaster software shows the maximum Root Mean Square (RMS) value of coordinate differences were 0.013 m in very high resolution, 0.017 m in high resolution, 0.031 m in medium resolution and 0.052 m in low resolution respectively. Meanwhile, the accuracy of TLS point cloud data between picking point manually and total station data using intersection method shows the maximum RMS values of coordinate differences were 0.013 m in very high resolution, 0.018 m in high resolution, 0.033 m in medium resolution and 0.054 m in low resolution respectively. Hence, it can be concluded that the high or very high resolution is needed for landslide monitoring using Topcon GLS-2000 which can provide more accurate data in slope result, while the low and medium resolutions is not suitable for landslide monitoring due to the accuracy of TLS point cloud data that will decreased when the resolution value is increased.

Natural systems, driven by complex processes, exhibit variations in their behavior across different time scales. These variations often follow power-law relationships, providing valuable insights into the underlying physical mechanisms. This study investigates the scaling and multifractal behavior of precipitation using high-resolution (1-minute) data from nine stations across diverse climatic zones in Istanbul. By using high-resolution data, the study investigates precipitation scaling at sub-daily time scales, which is crucial for understanding short-duration rainfall events and their potential impacts on urban areas like Istanbul. The research employs a combination of spectral scaling, detrended fluctuation analysis (DFA), wavelet variance, and multifractal analysis to examine precipitation scaling. The calculated scaling exponents were then compared to identify potential interdependencies. Notably, the multifractal analysis revealed an apparent multifractal character in the precipitation data for all stations. The results of the study indicate that the precipitation data exhibits multifractal characteristics at all stations, with spectral scaling exponents ranging from 0.58 to 0.98 and Hölder exponents tending towards 0.5, indicating weak positive serial correlations. The findings of this research have important implications for various hydrological applications, including the development of intensity-duration-frequency (IDF) curves and the evaluation of global circulation models (GCMs).

The resolution of seismic data determines the ability to characterize individual geological structures in a seismic image. Sparse spike inversion (SSI) is an effective approach for improving the resolution of seismic data. However, the basic assumption of SSI is that the strong reflectivity of the formation is sparse, which may not be a reasonable fit for weak thin-layer reflections. In this study, we propose a deep learning-based method to reconstruct high-resolution seismic data by combining information from the longitudinal reflectivity distribution and lateral geological structure features in the field data. A U-shaped network that fuses residual block and attention mechanism is used to learn the relationship between low resolution and high resolution. In addition, we use a hybrid loss function that combines <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\ell _{1}}$ </tex-math></inline-formula> loss and structural similarity (SSIM) loss to optimize the network parameters for better distinguishing the geometrical features characterized by structural amplitude changes. To train the network, we adopt a workflow to automatically generate numerous 2-D low-resolution data and their corresponding high-resolution data. In this workflow, the prior information, such as statistical reflectivity distribution of well logs and structural features of the data are considered. The synthetic data and field data tests show that our method can work well compared to the traditional method even though only a few well logs are available. Especially in the field data example, our method recovers thin layers better and yields laterally more consistent high-resolution results.

Initialisation using high spatial, temporal and spectral resolution satellite observations

MODIS and ASTER synergy for characterizing thermal volcanic activity

Urban green is recognized as an important functional element of the city, which affects directly the standard of living. The present paper is concerned with the study of urban green by means of object-oriented image analysis of high-resolution IKONOS data. More specifically, the potential for detecting urban green and quantitatively assessing it was explored. The analysis included two levels of segmentation and classification. On the first level, objects to which the image was segmented were subsequently classified according to a vegetation index (Scaled MSAVI) to areas with dense, thin or no vegetation. On the second level the image was classified in larger areas that simulated building blocks according to the relative area of vegetation, in order to create a thematic map of urban green density. The evaluation of the results indicated that detection and quantitative assessment of urban green was achieved with satisfactory accuracy. The use of additional data (DEM, hyperspectral, GIS) will allow a more detail study of the urban green from high resolution data by means of object-oriented image analysis

This paper proposes a new technique to detect the urban slums from urban buildings using very high resolution data. Many cities in the Global South are facing the development and growth of highly dynamic slum areas, but often lack detailed spatial information. Unlike buildings, vegetation and other features, urban slums lack in their unique spectral signatures. Thus, accurate detection of slums using remote sensing data poses real challenge to researchers and decision-makers. In this work, gray-level co-occurrence matrix, Tamura-based statistical feature extraction and wavelet frame transform-based spectral feature extraction techniques are proposed for detecting the urban slums from urban buildings. The very high resolution data of Madurai city, South India, acquired by Worldview-2 sensor (1.84 m) proved the ability of the proposed approaches to identify urban slums from urban buildings. Experimental results demonstrate that the proposed wavelet frame transform-based approach can generate higher classification accuracy than other approaches.

Small-scale fisheries (SSF, boats < 12 m) represent 90% of this sector at a worldwide scale and 84% of the EU fleet. Mapping the areas and intensity where the fishing operations occur is essential for spatial planning, safety, fisheries sustainability and biodiversity conservation. The EU is currently regulating position tracking of SSF fishing vessels requiring precision resolved geo-positional data (sec to min resolution).Here we developed a series of procedures aimed at categorizing fishing boats behaviour using high resolution data. Our integrated approach involve novel routines aimed at (i) produce an expert validated data set, (ii) pre-processing of positional data, (iii) establishing minimal required temporal resolution, and (iv) final assessment of an optimized classification model. Objective (iv) was implemented by using statistical and machine learning (ML) routines, using novel combinations of fixed thresholds estimates using regression trees and classification methods based on anti-mode, Gaussian Mixture Models (GMM), Expectation Maximisation (EM) algorithms, Hidden Markov Models (HMM) and Random Forest (RF). Of relevance, the final evaluation framework incorporates both error quantification and fishing effort indicators. We tested the method by running through four SSF fisheries from Portugal recorded every 30 sec, with 183 boat trips validated, and concluded that the more robust time interval for data acquisition in these metiers should be <2 min and that mode and random forest methods with pre-data treatment gave the best results. A special effort was concentrated in a visual support provided by the results produced by this new method, making its interpretation easier, thus facilitating transference and translation into other fishery levels. After the current validation in the Portuguese SSF fleet, we posit that our novel procedure has the potential to serve as an integrated quantitative approach to the EU SSF management.

High resolution three-dimensional numerical modelling of rockfalls

This paper highlights the requirement for very high resolution (&lt;0·25 m) elevation data for quantitative and qualitative morphometric analyses. Traditional techniques for high resolution data capture (e.g. airborne, heliborne) are prohibitively expensive for small studies and therefore a kite‐based platform was developed, in conjunction with a consumer non‐metric digital camera, for data capture. The combination of kite and digital camera is more generally termed kite aerial photography (KAP). The accuracy of data derived by digital photogrammetry and imagery acquired using a kite based non‐metric camera is assessed by three experiments: one on smooth terrain, one on tor terrain and one on a glaciofluvial esker. Ground control targets were surveyed at all three sites, with the imagery subsequently processed using the Leica Photogrammetry Suite. The results demonstrate that the method can extract a high number of sampling points at high accuracy, provided that there is suitable image texture across the site. However, final judgment concerning the suitability of derived data is dependent upon an understanding of measurement variability and user quantification of acceptable accuracy. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Forest canopy structure can be described by a variety of biophysical parameters, for example leaf area index (LAI) and fractional cover (fcover). These parameters are derived currently from remotely sensed data only with limited accuracy. The retrieval of biophysical parameters is often conducted by empirical models based on vegetation indices (VI) exploiting the spectral information but ignoring the spatial dimension contained in remote sensing data. However, texture information provided by high spatial resolution data can be potentially used as additional information related to the forest structure and might improve the models for the retrieval of biophysical parameters. The aim of this research is to evaluate several methods to combine spectral and textural information to derive the best retrieval method of LAI and fcover from spectral and high spatial resolution remote sensing data in a coniferous forest in the Swiss National Park. Spectral data as well as spatial data contain information, which can be correlated with the field measurements of biophysical forest parameters. The relationship between spectral data and the field measurements proved to be slightly better than between spatial data and field parameter.

The task of drilling and completing horizontal development wells in carbonate reservoirs can prove to be quite difficult, especially in the well placement and completion design stages. These stages of well construction can be particularly challenging when the reservoir is characterized by natural fracture systems which can result in drilling fluid losses, or when drilling with narrow TVD targets. The utilization of high resolution borehole images to optimize petrophysical evaluation, well placement, and completion design becomes a necessity for reservoir evaluation team. This paper will present a case study of how this new LWD technology optimized well placement, and completion design. During the pre-job planning stage, numerous scenarios are considered to assist in determining which "bundle" of logging technology is best suited to achieve the objectives of drilling the well. Optimizing logging design specifications required to meet the objectives may not be a simple job. For instance, placing the lateral near the roof of the reservoir while getting resistivity measurements that are least affected by overlying beds would seem to be irreconcilable. Similarly, acquiring high quality resolution image data in the event of borehole deterioration associated with total drilling fluid losses is a difficult task to achieve. In this case study, the technology is shown to successfully "geo-steer" within a single thin layer of the reservoir and achieved a 100% net-to-gross while simultaneously placing the wellbore near the roof. The real-time high resolution image was of sufficient quality used to identify sub-seismic faults, natural conductive fractures, karst dissolution features, healed fractures and drilling induced fractures as the well was being drilled. Once the well was drilled to the planned total depth, the high resolution image data could be used for the completion design, thereby saving the time, expense, and risk of an additional logging run using pipe-conveyed wireline logging tools. In addition to the completion design, high confidence dip data were used to update the geological models, fracture porosity and aperture were computed from the images and the shallow focused resistivity measurements were used for more accurate saturation calculations.

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño–Southern Oscillation (ENSO) is considered a significant signal of the global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed-resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, the influence of the month from which the low-resolution sea surface temperature data are obtained. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

Introduction Dynamic MRI may be used to image motion that is needed for accurate radiotherapy treatment planning. The tradeoff between temporal and spatial resolution has been a major issue affecting the use of dynamic MRI. Here we propose to improve temporal resolution by reconstructing high spatial resolution components with deformably registered motion data. Dynamic MRI with increased temporal resolution without scarifying spatial resolution for several sites are presenting. Methods and Materials High and low spatial resolution components of the image typically move similarly, i.e. share most of the motion vector information. The motion vectors can be obtained by deformable registration of the central k‐space, and applied to the high spatial resolution data. This allows one to acquire the peripheral k‐space at a lower temporal resolution than the central k‐space therefore increase the overall sampling rate. We tested the possible gain by this method for 2‐dimensional dynamic MRIs (SIEMENS 3T Verio) of swallowing, breathing and peristalsis. Results The mean error in the displacement vectors obtained using only the central 20% of k‐space vs. using the full k‐space was less than 0.5mm, which reduced to 0.2mm if 50% of the radius is used. Peristalsis motion with sudden changes shared least information between central and peripheral k‐space. The reconstructed images with as low as central 20% of the k‐space radius are very similar to images reconstructed with full k‐space. The resulting probability distance histograms and regions of interest show minimal variation. Conclusion Temporal resolution can be improved considerably without compromising the spatial resolution if the low‐resolution vector information is used to construct high spatial resolution data as demonstrated in this work. The deformable registration accuracy is highly critical for the overall performance of the method. Conflict of Interest: This work is supported partially by Siemens Healthcare.