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—There is great potential for the development of remote sensing methods that integrate and exploit both multispec-tral and multitemporal information. This paper presents a new image processing method: Spectral–Temporal Analysis by Response Surface (STARS), which synthesizes the full information content of a multitemporal–multispectral remote sensing image(More)
The National Geomatics Center of China (NGCC) produced Global Land Cover (GlobalLand30) maps with 30 m spatial resolution for the years 2000 and 2009–2010, responding to the need for harmonized, accurate, and high-resolution global land cover data. This study aims to assess the mapping accuracy of the land surface water layer of GlobalLand30 for 2009–2010.(More)
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Developments in Geographic Information Technology have raised the expectations of users. A static map is no longer enough; there is now demand for a dynamic representation. Time is of great importance when operating on real world geographical phenomena, especially when these are dynamic. Researchers in the field of Temporal Geographical Information Systems(More)
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Much ecological analysis requires detailed spatial observation, traditionally conducted through field measurement. Remote sensing has been tested extensively as a means of ecological investigation, but many such studies were limited by the relatively coarse spatial resolution of the imagery used. The new generation of fine spatial resolution satellite(More)
This paper presents a review of object-based image classification, outlining recent developments, assessing current capabilities and signposting future implications. Object-based classification methods are described, outlining advantages over other forms of classification, and limitations in the object-based approach. Examples are provided from the UK,(More)
This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts' knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, BayNeRD(More)
Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach(More)
A method for improving artificial neural network performance by using multi-temporal, multi-spectral and multi-source remotely-sensed data as features for classifying agricultural crops is described. The procedure characterizes all the pixels in a scene by considering their intensity values as a function of time of imaging and spectral waveband. An(More)