Effects of spatial correlations and global precedence on the visual fidelity of distorted images

2006 ◽  
Author(s):  
Damon M. Chandler ◽  
Kenny H. Lim ◽  
Sheila S. Hemami
Keyword(s):  
Spatial Correlations ◽  
Global Precedence ◽  
Visual Fidelity

Quadratic Discriminant Analysis of Spatially Correlated Data

2001 ◽  
Vol 6 (2) ◽  
pp. 15-28 ◽  
Author(s):  
K. Dučinskas ◽  
J. Šaltytė
Keyword(s):  
Discriminant Function ◽  
Error Rate ◽  
Gaussian Random Field ◽  
Correlated Data ◽  
Covariance Matrices ◽  
Classification Rule ◽  
Spatial Correlations ◽  
Linear Quadratic ◽  
Spatial Correlation Function ◽  
Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

scholarly journals Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Information Aggregation ◽  
Semantic Segmentation ◽  
Spatial Correlations ◽  
Convolutional Network ◽  
Object Based ◽  
Graph Neural Networks ◽  
Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

Reducing multipath effect of low-cost GNSS receivers for monitoring by considering temporal correlations

2020 ◽  
Vol 14 (2) ◽  
pp. 167-175
Author(s):  
Li Zhang ◽  
Volker Schwieger
Keyword(s):  
Low Cost ◽  
Ground Plane ◽  
Single Frequency ◽  
Spatial Correlations ◽  
Raw Data ◽  
Multipath Effect ◽  
Temporal Correlations ◽  
Multipath Effects ◽  
Gnss Receivers ◽  
Multipath Signals

AbstractThe investigations on low-cost single frequency GNSS receivers at the Institute of Engineering Geodesy (IIGS) show that u-blox GNSS receivers combined with low-cost antennas and self-constructed L1-optimized choke rings can reach an accuracy which almost meets the requirements of geodetic applications (see Zhang and Schwieger [25]). However, the quality (accuracy and reliability) of low-cost GNSS receiver data should still be improved, particularly in environments with obstructions. The multipath effects are a major error source for the short baselines. The ground plate or the choke ring ground plane can reduce the multipath signals from the horizontal reflector (e. g. ground). However, the shieldings cannot reduce the multipath signals from the vertical reflectors (e. g. walls).Because multipath effects are spatially and temporally correlated, an algorithm is developed for reducing the multipath effect by considering the spatial correlations of the adjoined stations (see Zhang and Schwieger [24]). In this paper, an algorithm based on the temporal correlations will be introduced. The developed algorithm is based on the periodic behavior of the estimated coordinates and not on carrier phase raw data, which is easy to use. Because, for the users, coordinates are more accessible than the raw data. The multipath effect can cause periodic oscillations but the periods change over time. Besides this, the multipath effect’s influence on the coordinates is a mixture of different multipath signals from different satellites and different reflectors. These two properties will be used to reduce the multipath effect. The algorithm runs in two steps and iteratively. Test measurements were carried out in a multipath intensive environment; the accuracies of the measurements are improved by about 50 % and the results can be delivered in near-real-time (in ca. 30 minutes), therefore the algorithm is suitable for structural health monitoring applications.

scholarly journals Spatiotemporal Effects and Driving Factors of Water Pollutants Discharge in Beijing–Tianjin–Hebei Region

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1174
Author(s):  
Qilong Ren ◽  
Hui Li
Keyword(s):  
Economic Development ◽  
Industrial Structure ◽  
Oxygen Demand ◽  
Ammonia Nitrogen ◽  
Pollutant Emissions ◽  
Spatial Correlations ◽  
Time Data ◽  
Spatiotemporal Evolution ◽  
Water Pollutants ◽  
Pollutant Discharge

The problem of water pollution is a social issue in China requiring immediate and urgent solutions. In the Beijing–Tianjin–Hebei region, the contradiction between preserving the ecological environment and facilitating sustainable economic development is particularly acute. This study analyzed the spatiotemporal evolution of water pollutants and their factors of influence using statistics on the discharge of two water pollutants, namely chemical oxygen demand (COD) and NH3-N (ammonia nitrogen), in 154 counties in both 2012 and 2016 as research units in the region. The study employed Exploratory Spatial-Time Data Analysis (ESTDA), Standard Deviational Ellipse (SDE), and the Geographically Weighted Regression (GWR) models, as well as ArcGIS and GeoDa software, obtaining the following conclusions: (1) From 2012 to 2016, pollutant discharge dropped significantly, with COD and NH3-N emissions decreasing 65.9% and 47.2%, respectively; the pollutant emissions possessed the spatial feature of gradual gradient descent from the central districts to the periphery. (2) The water pollutants discharge displayed significant and positive spatial correlations. The spatiotemporal cohesion of the spatiotemporal evolution of the pollutants was higher than their spatiotemporal fluidity, representing strong spatial locking. (3) The level of economic development, the level of urbanization, and the intensity of agricultural production input significantly and positively drove pollutant discharge; the environmental regulations had a significant effect on reducing the emission of pollutants. In particular, the effect for NH3-N emissions reduction was stronger; the driving effect of the industrial structure and the distance decay was not significant.

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