Advances in remote sensing and GIS analysis
An authoritative and state-of-the-art book bringing together some of the most recent developments in remote sensing and GIS analysis with a particular emphasis on mathematical techniques and their applications.
With contributions from academia, industry and research institutes, all with a high standing, this book covers a range of techniques including: fuzzy classification, artificial neural networks, geostatistical techniques (such as kriging, cokriging, stochastic simulation and regularization, texture classification, fractals, per-parcel classification, raster and vector data integration and process modelling. The range of applications includes land cover and land use mapping, cloud tracking, snow cover mapping and air temperature monitoring, topographic mapping, geological classification and soil erosion modelling.
This book will be valuable to both researchers and advanced students of remote sensing and GIS. It contains several new approaches, recent developments, and novel applications of existing techniques. Most chapters report the results of experiment and investigation. Some chapters form broad reviews of recent developments in the field. In all cases, the mathematical basis is fully explained.
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Land Cover Classification Revisited
Cloud Motion Analysis
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algorithm application approach artificial neural network Atkinson AVIRIS Barnsley boundaries calculated classification accuracy cloud motion coefficients cokriging computed correlation cover fraction cover map cross-variograms data model data set defined density derived distribution Engineering and Remote error estimate example field Figure Foody fractal function fuzzy classification Geographical Information Systems geostatistical global identified IDWS IEEE Transactions image classification imaging spectrometer integrated International Journal interpolation Journal of Remote kriging mapping maximum likelihood measurements Meteosat method minimum air temperatures mixed pixels multispectral NDVI noise nugget variance output overland flow parameters per-field classification pixels polygon problem rainfall raster regions Remote Sensing remotely sensed data remotely sensed images represent root mean square sample satellite sensor scale segmentation semivariance sensed data sensed imagery sensing and GIS snow cover snow depth spatial resolution spectral classification spectral wavebands statistics techniques thematic threshold urban values variable variation variogram vector data vegetation cover