The Experts below are selected from a list of 43986 Experts worldwide ranked by ideXlab platform
Peter M. Atkinson - One of the best experts on this subject based on the ideXlab platform.
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A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis.
IEEE transactions on pattern analysis and machine intelligence, 2020Co-Authors: Plamen Angelov, Ce Zhang, Peter M. AtkinsonAbstract:Large-scale {(large-area)}, fine spatial resolution Satellite Sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use categories may appear at the same region. Autonomous information extraction from these images is essential in the field of pattern recognition within remote sensing, but this task is extremely challenging due to the spectral and spatial complexity captured in Satellite Sensor imagery. In this research, a semi-supervised deep rule-based approach for Satellite Sensor image analysis (SeRBIA) is proposed, where large-scale Satellite Sensor images are analysed autonomously and classified into detailed land-use categories. Using an ensemble feature descriptor derived from pre-trained AlexNet and VGG-VD-16 models, SeRBIA is capable of learning continuously from both labelled and unlabelled images through self-adaptation without human involvement or intervention. Extensive numerical experiments were conducted on both benchmark datasets and real-world Satellite Sensor images to comprehensively test the validity and effectiveness of the proposed method. The novel information mining technique developed here can be applied to analyse large-scale Satellite Sensor images with high accuracy and interpretability, across a wide range of real-world applications.
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intercomparison of Satellite Sensor land surface phenology and ground phenology in europe
Geophysical Research Letters, 2015Co-Authors: Victor Rodriguezgaliano, Jadunandan Dash, Peter M. AtkinsonAbstract:Land surface phenology (LSP) and ground phenology (GP) are both important sources of information for monitoring terrestrial ecosystem responses to climate changes. Each measures different vegetation phenological stages and has different sources of uncertainties, which make comparison in absolute terms challenging, and therefore, there has been limited attempts to evaluate the complementary nature of both measures. However, both LSP and GP are climate driven and therefore should exhibit similar interannual variation. LSP obtained from the whole time series of Medium-Resolution Imaging Spectrometer data was compared to thousands of deciduous tree ground phenology records of the Pan European Phenology network (PEP725). Correlations observed between the interannual time series of the Satellite Sensor estimates of phenology and PEP725 records revealed a close agreement (especially for Betula Pendula and Fagus Sylvatica species). In particular, 90% of the statistically significant correlations between LSP and GP were positive (mean R2 = 0.77). A large spatiotemporal correlation was observed between the dates of the start of season (end of season) from space and leaf unfolding (autumn coloring) at the ground (pseudo R2 of 0.70 (0.71)) through the application of nonlinear multivariate models, providing, for the first time, the ability to predict accurately the date of leaf unfolding (autumn coloring) across Europe (root-mean-square error of 5.97 days (6.75 days) over 365 days).
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Predicting socioeconomic conditions from Satellite Sensor data in rural developing countries: A case study using female literacy in Assam, India
Applied Geography, 2013Co-Authors: Gary R. Watmough, Peter M. Atkinson, Craig W. HuttonAbstract:Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed Satellite Sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed Satellite Sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GARerr) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GARerr model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution Satellite Sensor data and methods that can account for such complexities.
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inter comparison of four models for smoothing Satellite Sensor time series data to estimate vegetation phenology
Remote Sensing of Environment, 2012Co-Authors: Peter M. Atkinson, C Jeganathan, Jadu Dash, Clement AtzbergerAbstract:Several models have been fitted in the past to smooth time-series vegetation index data from different Satellite Sensors to estimate vegetation phenological parameters. However, differences between the models and fine tuning of model parameters lead to potential differences, uncertainty and bias between the results amongst users. The current research assessed four techniques: Fourier analysis, asymmetric Gaussian model, double logistic model and the Whittaker filter for smoothing multi-temporal Satellite Sensor observations with the ultimate purpose of deriving an appropriate annual vegetation growth cycle and estimating phenological parameters reliably. The research used Level 3 Medium Resolution Imaging Spectrometer (MERIS, spatial resolution ~ 4.6 km) Terrestrial Chlorophyll Index (MTCI) data over the years 2004 to 2006 composited at eight day intervals covering the Indian sub-continent. First, the four models were fitted to representative sample time-series of the major vegetation types in India, and the quality of the fit was analysed. Second, the effect of noise on model fitting was analysed by adding Gaussian noise to a standard profile. Finally, the four models were fitted to the whole study area to characterise variation in the quality of model fitting as a function of single and double vegetation seasons. These smoothed data were used to estimate the onset of greenness (OG), a major phenological parameter. The models were evaluated using the root mean square error (RMSE), Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC). The first test (fitting to representative sample time series) revealed the consistently superior performance of the Whittaker and Fourier approaches in most cases. The second test (fitting after the addition of Gaussian noise) revealed the superior performance of the double logistic and Fourier approaches. Finally, when the approaches were applied to the whole study, thus, including vegetation with different phenological profiles and multiple growing seasons (third test), it was found that it was necessary to tune each of the models according to the number of annual growing seasons to produce reliable fits. The double logistic and asymmetric Gaussian models did not perform well for areas with more than one growing season per year. The mean absolute deviation in OG derived from these models was a maximum (3 to 4 weeks) within the dry deciduous vegetation type and minimum (1 week) in evergreen vegetation. All techniques yielded consistent results over the south-western and north-eastern regions of India characterised by tropical climate.
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sub pixel mapping of rural land cover objects from fine spatial resolution Satellite Sensor imagery using super resolution pixel swapping
International Journal of Remote Sensing, 2006Co-Authors: M. W. Thornton, Peter M. Atkinson, D A HollandAbstract:Mapping rural land cover features, such as trees and hedgerows, for ecological applications is a desirable component of the creation of cartographic maps by the Ordnance Survey. Based on the phenomenon of spatial dependence, sub‐pixel mapping can provide increased mapping accuracy of such features. A simple pixel‐swapping algorithm for sub‐pixel mapping was applied to soft classified fine spatial resolution remotely sensed imagery. Initially, QuickbirdTM Satellite Sensor imagery with a spatial resolution of 2.6 m was acquired of the Christchurch area of Dorset, UK, and three field sites chosen. The imagery was soft classified using a supervised fuzzy c‐means algorithm and then super‐resolved into sub‐pixels using a zoom factor of five. Sub‐pixels within pixels were then iteratively swapped until the spatial correlation between sub‐pixels for the entire image was maximized. Mathematical morphology was used to suppress error in the super‐resolved output, increasing overall accuracy. Field data, including de...
Nengcheng Chen - One of the best experts on this subject based on the ideXlab platform.
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A Sharable and Interoperable Meta-Model for Atmospheric Satellite Sensors and Observations
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012Co-Authors: Nengcheng ChenAbstract:How the heterogeneous and distributed atmospheric Satellite Sensors can achieve precise discovery and collaborative observation is a big challenge. In this study, we propose an atmospheric Satellite Sensor observation system meta-model that reuses and extends the existing geospatial or Sensor-related metadata standards to enable the sharing and interoperability of atmospheric Satellite Sensors. The Open Geospatial Consortium Sensor Model Language (SensorML) has a clear hierarchy in describing the metadata framework, and it is adopted as the carrier to formalize our proposed meta-model into the Atmospheric Satellite Sensor Observation Information Model (A-SSOIM). Three different types of atmospheric Satellite Sensors are used to test the versatility of the proposed meta-model and the applicability of this formal expression of A-SSOIM. Results show that the proposed meta-model can be reused in all kinds of atmospheric Satellite Sensors to enable the sharing of atmospheric Satellite Sensor information and potentially promoting the interoperability of these Satellite Sensors.
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IGARSS - Remote sensing Satellite Sensor information retrieval and visualization based on SensorML
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011Co-Authors: Nengcheng Chen, Chao WangAbstract:In the era of high-frequency occurrence of natural disasters, users are more urgently concerned with the sharing of the Satellite Sensor resources information and coordinating the complement of Sensor observation. However, the capacity of discovering, retrieving and visualizing the Sensor resource information accurately based on heterogeneous Sensors over Sensor network is very limited. This paper proposes the system architecture for effectively managing those heterogeneous and multiple Sensors and their information, which is inspired by the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) Initiative and based on one of its information model—Sensor Model Language (SensorML) of which Process Model is the core. The prototype “SensorModel V1.0” is designed and implemented used to construct the standard model for unified management of multiple remote sensing Satellite Sensor resources information and demonstrate the model-based retrieval and visualization of related remote Sensors and their information, which promotes the comprehensive accessing and collaborative planning/controlling the available remote Sensor's information in time-critical disaster emergency.
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Geoinformatics - Asynchronous Sensor planning service chain driven dynamical Satellite Sensor information retrieval
2010 18th International Conference on Geoinformatics, 2010Co-Authors: Chao Yang, Zeqiang Chen, Nengcheng Chen, Liping DiAbstract:In a Sensor Web environment, Sensors are heterogonous. The Sensor planning service offers a standardized interface for the control of Sensors and simulations. But some complex Sensors like Satellite need midterm or long-term action to response a given task, so, adding asynchronous support in SPS is necessary. The workflow description language can chain asynchronous web service together while it has to contend with the complex and specific communication mechanism of asynchronous SPS. In order to achieve asynchronous SPS, the Asynchronous Sensor Planning Service Chain (ASPSC) is proposed in this paper. The internal message exchange and external callback invocation method is used to serve dynamical retrieval service of Satellite planning information under Sensor Web environment through the chaining of SPS operation. The proposed method has been successfully demonstrated in application scenarios for Simplified General Perturbations Satellite Orbit Model 4(SGP-4). It can service most near earth Satellites and offers an automatic Sensor information retrieval. Comparing to the traditional SPS, it is more flexible and strength in software architecture.
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Remote Satellite Sensor modeling design and implementation based on the Sensor modeling language
International Symposium on Spatial Analysis Spatial-Temporal Data Modeling and Data Mining, 2009Co-Authors: Jiaying Chen, Nengcheng Chen, Wei Wang, Zhong ZhengAbstract:Various Sensors play a very important role in our everyday life. Sensor model language is a vital part of the OGC for the description of the common Sensor. SensorML not only describe properties of the Sensor itself, but also the process about the Sensor. Based on the SensorML which is coding with XML schema, we need an efficient way for modeling a Sensor or a system. In this paper, firstly, the SensorML model is introduced, in which the SensorML schema is analyzed, and a common procedure for Sensor modeling is defined. Secondly, the establishment of a common Sensor model platform comes true using the dynamic generation, reflex, XML DOM in the .NET Framework 3.5. At last, the BJ-1 Satellite was used as an example for the SensorML modeling. For the non-physical process chain, the red band and near infrared band of the BJ-1 Satellite were as inputs for the NDVI calculation. After all, we validate the BJ-1 Satellite SensorML instance.
Lars Eklundh - One of the best experts on this subject based on the ideXlab platform.
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timesat a program for analyzing time series of Satellite Sensor data
Computers & Geosciences, 2004Co-Authors: P Jonsson, Lars EklundhAbstract:Three different least-squares methods for processing time-series of Satellite Sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky-Golay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI and seasonal amplitudes. Based on general principles, the TIMESAT program can be used also for other types of Satellite-derived time-series data.
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seasonality extraction by function fitting to time series of Satellite Sensor data
IEEE Transactions on Geoscience and Remote Sensing, 2002Co-Authors: P Jonsson, Lars EklundhAbstract:A new method for extracting seasonality information from time-series of Satellite Sensor data is presented. The method is based on nonlinear least squares fits of asymmetric Gaussian model functions to the time-series. The smooth model functions are then used for defining key seasonality parameters, such as the number of growing seasons, the beginning and end of the seasons, and the rates of growth and decline. The method is implemented in a computer program TIMESAT and tested on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data over Africa. Ancillary cloud data [clouds from AVHRR (CLAVR)] are used as estimates of the uncertainty levels of the data values. Being general in nature, the proposed method can be applied also to new types of Satellite-derived time-series data.
P. Marshall - One of the best experts on this subject based on the ideXlab platform.
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Updating topographic mapping in Great Britain using imagery from high-resolution Satellite Sensors
ISPRS Journal of Photogrammetry and Remote Sensing, 2006Co-Authors: David A. Holland, Doreen S Boyd, P. MarshallAbstract:Topographic mapping from remotely sensed imagery is carried out all over the world, using data from an ever-growing number of Sensors. Traditional film cameras are gradually being replaced by digital cameras and scanners, but most topographic mapping still relies on Sensors based on airborne platforms. This paper examines the potential of high resolution Satellite Sensor imagery for the updating of topographic mapping, from the perspective of a national mapping agency. After a review of Satellites capable of being used for this purpose, several examples of mapping projects are presented. The paper ends with a look to the future, and asks whether Satellite imagery can ever replace airborne (digital or analogue) photography for the makers of maps. It is concluded that high resolution Satellite Sensor imagery does have a role to play in the update of topographic mapping, especially in the detection of change.
P Jonsson - One of the best experts on this subject based on the ideXlab platform.
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timesat a program for analyzing time series of Satellite Sensor data
Computers & Geosciences, 2004Co-Authors: P Jonsson, Lars EklundhAbstract:Three different least-squares methods for processing time-series of Satellite Sensor data are presented. The first method uses local polynomial functions and can be classified as an adaptive Savitzky-Golay filter. The other two methods are more clear cut least-squares methods, where data are fit to a basis of harmonic functions and asymmetric Gaussian functions, respectively. The methods incorporate qualitative information on cloud contamination from ancillary datasets. The resulting smooth curves are used for extracting seasonal parameters related to the growing seasons. The methods are implemented in a computer program, TIMESAT, and applied to NASA/NOAA Pathfinder AVHRR Land Normalized Difference Vegetation Index data over Africa, giving spatially coherent images of seasonal parameters such as beginnings and ends of growing seasons, seasonally integrated NDVI and seasonal amplitudes. Based on general principles, the TIMESAT program can be used also for other types of Satellite-derived time-series data.
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seasonality extraction by function fitting to time series of Satellite Sensor data
IEEE Transactions on Geoscience and Remote Sensing, 2002Co-Authors: P Jonsson, Lars EklundhAbstract:A new method for extracting seasonality information from time-series of Satellite Sensor data is presented. The method is based on nonlinear least squares fits of asymmetric Gaussian model functions to the time-series. The smooth model functions are then used for defining key seasonality parameters, such as the number of growing seasons, the beginning and end of the seasons, and the rates of growth and decline. The method is implemented in a computer program TIMESAT and tested on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data over Africa. Ancillary cloud data [clouds from AVHRR (CLAVR)] are used as estimates of the uncertainty levels of the data values. Being general in nature, the proposed method can be applied also to new types of Satellite-derived time-series data.
