The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform

Ann Vreeland Watkins - One of the best experts on this subject based on the ideXlab platform.

Xiaoqiang Lu - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection
    IEEE Transactions on Geoscience and Remote Sensing, 2019
    Co-Authors: Yuanlin Zhang, Yuan Yuan, Yachuang Feng, Xiaoqiang Lu
    Abstract:

    Object detection is a basic issue of very high-resolution remote sensing images (RSIs) for automatically labeling objects. At present, deep learning has gradually gained the competitive advantage for remote sensing object detection, especially based on convolutional neural networks (CNNs). Most of the Existing methods use the global information in the fully connected feature vector and ignore the local information in the convolutional feature cubes. However, the local information can provide spatial information, which is helpful for accurate localization. In addition, there are variable factors, such as rotation and scaling, which affect the object detection accuracy in RSIs. In order to solve these problems, this paper presents a hierarchical robust CNN. First, multiscale convolutional features are extracted to represent the hierarchical spatial semantic information. Second, multiple fully connected layer features are stacked together so as to improve the rotation and scaling robustness. Experiments on two Data Sets have shown the effectiveness of our method. In addition, a large-scale high-resolution remote sensing object detection Data Set is established to make up for the current situation that the Existing Data Set is insufficient or too small. The Data Set is available at https://github.com/CrazyStoneonRoad/TGRS-HRRSD-DataSet.

I.t. Phillips - One of the best experts on this subject based on the ideXlab platform.

  • ICDAR - How to extend and bootstrap an Existing Data Set with real-life degraded images
    Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), 1999
    Co-Authors: I.t. Phillips
    Abstract:

    This paper introduces a methodology for bootstrapping and creating large number of groundtruthed "real-life" degraded images from an Existing Data Set with a fraction of the original cost and time. The real-life degradations include geometric distortions, coffee stains, water or ink marks, and folds and creases. The methodology includes an automatic procedure to generate unlimited "real-life" degraded images (with coffee and ink marks and soil spots) without any cost. A small experiment was conducted to illustrate the effectiveness of our methodology. In the experiment, 22 real-life degraded images and the two original images were tested on a commercial OCR system. The accuracy rates of the OCR for the two original pages are 98.46% and 99.34% while the accuracy rates for the degraded pages are ranging from 57.17% to 98.45%, depending on the severity and the type of degradation applied to the pages.

  • How to extend and bootstrap an Existing Data Set with real-life degraded images
    Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318), 1999
    Co-Authors: I.t. Phillips
    Abstract:

    This paper introduces a methodology for bootstrapping and creating large number of groundtruthed "real-life" degraded images from an Existing Data Set with a fraction of the original cost and time. The real-life degradations include geometric distortions, coffee stains, water or ink marks, and folds and creases. The methodology includes an automatic procedure to generate unlimited "real-life" degraded images (with coffee and ink marks and soil spots) without any cost. A small experiment was conducted to illustrate the effectiveness of our methodology. In the experiment, 22 real-life degraded images and the two original images were tested on a commercial OCR system. The accuracy rates of the OCR for the two original pages are 98.46% and 99.34% while the accuracy rates for the degraded pages are ranging from 57.17% to 98.45%, depending on the severity and the type of degradation applied to the pages.

Yuanlin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection
    IEEE Transactions on Geoscience and Remote Sensing, 2019
    Co-Authors: Yuanlin Zhang, Yuan Yuan, Yachuang Feng, Xiaoqiang Lu
    Abstract:

    Object detection is a basic issue of very high-resolution remote sensing images (RSIs) for automatically labeling objects. At present, deep learning has gradually gained the competitive advantage for remote sensing object detection, especially based on convolutional neural networks (CNNs). Most of the Existing methods use the global information in the fully connected feature vector and ignore the local information in the convolutional feature cubes. However, the local information can provide spatial information, which is helpful for accurate localization. In addition, there are variable factors, such as rotation and scaling, which affect the object detection accuracy in RSIs. In order to solve these problems, this paper presents a hierarchical robust CNN. First, multiscale convolutional features are extracted to represent the hierarchical spatial semantic information. Second, multiple fully connected layer features are stacked together so as to improve the rotation and scaling robustness. Experiments on two Data Sets have shown the effectiveness of our method. In addition, a large-scale high-resolution remote sensing object detection Data Set is established to make up for the current situation that the Existing Data Set is insufficient or too small. The Data Set is available at https://github.com/CrazyStoneonRoad/TGRS-HRRSD-DataSet.

Andy Jarvis - One of the best experts on this subject based on the ideXlab platform.

  • Very high resolution interpolated climate surfaces for global land areas
    International Journal of Climatology, 2005
    Co-Authors: Robert J. Hijmans, Susan E. Cameron, Juan L. Parra, Peter G. Jones, Andy Jarvis
    Abstract:

    We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input Data were gathered from a variety of sources and, where possible, were restricted to records from the 1950-2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input Data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through Data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate Data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an Existing Data Set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution Data Sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the Data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation Data were used; and more information about spatial patterns of uncertainty in the Data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledge-based methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright � 2005 Royal Meteorological Society.