901 research outputs found

    Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

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    This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System(PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the IntegratedMultisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained

    Use of Semantic Technology to Create Curated Data Albums

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    One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discover tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out nonrelevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science

    Use of Semantic Technology to Create Curated Data Albums

    Get PDF
    One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out non-relevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science

    Deep learning in remote sensing: a review

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    Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

    Intelligent Data Analytics using Deep Learning for Data Science

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    Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization

    A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

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    In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

    Determination of atmospheric moisture structure and infrared cooling rates from high resolution MAMS radiance data

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    This program has applied Multispectral Atmospheric Mapping Sensor (MAMS) high resolution data to the problem of monitoring atmospheric quantities of moisture and radiative flux at small spatial scales. MAMS, with 100-m horizontal resolution in its four infrared channels, was developed to study small scale atmospheric moisture and surface thermal variability, especially as related to the development of clouds, precipitation, and severe storms. High-resolution Interferometer Sounder (HIS) data has been used to develop a high spectral resolution retrieval algorithm for producing vertical profiles of atmospheric temperature and moisture. The results of this program are summarized and a list of publications resulting from this contract is presented. Selected publications are attached as an appendix

    Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms

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    Harmful cyanobacteria blooms have been increasing in frequency throughout the world resulting in a greater need for water quality monitoring. Traditional methods of monitoring water quality, such as point sampling, are often resource expensive and time consuming in comparison to remote sensing approaches, however the spatial resolution of established water remote sensing satellites is often too coarse (300 m) to resolve smaller inland waterbodies. The fine scale spatial resolution and improved radiometric sensitivity of Landsat satellites (30 m) can resolve these smaller waterbodies, enabling their capability for cyanobacteria bloom monitoring. In this work, the utility of Landsat to retrieve concentrations of two cyanobacteria bloom pigments, chlorophyll-a and phycocyanin, is assessed. Concentrations of these pigments are retrieved using a spectral Look-Up-Table (LUT) matching process, where an exploration of the effects of LUT design on retrieval accuracy is performed. Potential augmentations to the spectral sampling of Landsat are also tested to determine how it can be improved for waterbody constituent concentration retrieval. Applying the LUT matching process to Landsat 8 imagery determined that concentrations of chlorophyll-a, total suspended solids, and color dissolved organic matter were retrieved with a satisfactory accuracy through appropriate choice of atmospheric compensation and LUT design, in agreement with previously reported implementations of the LUT matching process. Phycocyanin proved to be a greater challenge to this process due to its weak effect on waterbody spectrum, the lack of Landsat spectral sampling over its predominant spectral feature, and error from atmospheric compensation. From testing potential enhancements to Landsat spectral sampling, we determine that additional spectral sampling in the yellow and red edge regions of the visible/near-infrared (VNIR) spectrum can lead to improved concentration retrievals. This performance further improves when sampling is added to both regions, and when Landsat is transitioned to a VNIR imaging spectrometer, though this is dependent on band position and spacing. These results imply that Landsat can be used to monitor cyanobacteria blooms through retrieval of chlorophyll-a, and this retrieval performance can be improved in future Landsat systems, even with minor changes to spectral sampling. This includes improvement in retrieval of phycocyanin when implementing a VNIR imaging spectrometer

    Investigation using data from ERTS-1 to develop and implement utilization of living marine resources

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    The author has identified the following significant results. This 15-month ERTS-1 investigation produced correlations between satellite, aircraft, menhaden fisheries, and environmental sea truth data from the Mississippi Sound. Selected oceanographic, meteorological, and biological parameters were used as indirect indicators of the menhaden resource. Synoptic and near real time sea truth, fishery, satellite imagery, aircraft acquired multispectral, photo and thermal IR information were acquired as data inputs. Computer programs were developed to manipulate these data according to user requirements. Preliminary results indicate a correlation between backscattered light with chlorophyll concentration and water transparency in turbid waters. Eight empirical menhaden distribution models were constructed from combinations of four fisheries-significant oceanographic parameters: water depth, transparency, color, and surface salinity. The models demonstrated their potential for management utilization in areas of resource assessment, prediction, and monitoring
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