Geostatistical classification for remote sensing: an introduction

By Peter M. Atkinson, Philip Lewis. 2000

306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/174f4b6f9ce7f26a214861bd72b40b443d602bae

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Citations in the corpus

A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, methodology): “For a better explanation, the following sections provide the definition of PNI, steps of incorporating PNI into k-means classification technique, and results and conclusions.”
Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia (2013, methodology): “The probability of each pixel belonging to any given class was computed, and then the pixel was assigned to the class with the highest probability (Atkinson and Lewis 2000; Jensen 2005a).”
Hierarchical clustering of spatially correlated functional data (2012, methodology): “An overview about classification for geographically referenced data is given in Atkinson and Lewis (2000), where an example for remote sensing is shown.”
Object-based change detection (2012, methodology): “A commonly used image texture in the remote-sensing community is the statistical approach, which includes ﬁrst-order (e.g. standard deviation and skewness) and second-order (e.g. grey level co-occurrence matrix (GLCM) and semivariance) statistics (Haralick et al .”
Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia (2012, background): “The basic difference between these types of classification is the process by which the spectral characteristics of the different groupings are defined (Atkinson and Lewis 2000 ).”
Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health (2003, methodology): “Unlike pixel-by-pixel analysis, these techniques consider the spatial relationships between pixels (Atkinson & Lewis, 2000).”
Forest change detection by statistical object-based method (2006, background): “Indeed, the useful spatial, or contextual, information between the values of proximate pixels is most often ignored (Atkinson & Lewis, 2000; Townshend et al., 2000).”