A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification

By Fabio Pacifici, Marco Chini, William J. Emery. 2009

382 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/d01d03faff9356d76e308537246a2fc47932863e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

In Media Technology

Citations in the corpus

Slums from Space - 15 Years of Slum Mapping Using Remote Sensing (2016, background): “, [62,170,171]), which is also the basis for the “anisotropic rotation-invariant built-up presence index” (Pantex) [172].”
Object-based crop identification using multiple vegetation indices, textural features and crop phenology (2011, background): “Texture provides supplementary information about object properties that might be helpful for discrimination of heterogeneous crop-fields (Pacifici et al., 2009), although they had a disadvantage of high computational cost.”
Measuring the Effectiveness of Various Features for Thematic Information Extraction From Very High Resolution Remote Sensing Imagery (2015, background): “The firstand second-order textural features have similar importance [7].”
Building Instance Classification Using Street View Images (2018, background): “However, the transfer from land cover to land use in EO-data is complex and relies mostly on the geometry and the appearance of individual buildings and the patterns they group together [7, 8, 9, 10, 11, 12, 13, 14, 15, 16].”
Very High Resolution Multiangle Urban Classification Analysis (2012, background): “The six texture parameters are homogeneity, contrast, dissimilarity, entropy, second moment, and correlation [43].”
Vegetation classification method with biochemical composition estimated from remote sensing data (2011, background): “…data classifications: expert systems (Skidmore 1989, De Jong and Riezebos 1991), artificial neural networks (ANNs; Heerman and Khazenie 1992, Binagli et al. 2005, Rogan et al. 2008, Pacifici et al. 2009) and decision trees (Friedl and Brodley 1997, Kandrika and Roy 2008, Tooke et al. 2009).”
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, result): “With all the fourteen metrics developed by Haralick et al. [1973], six of them, including contrast, variance, correlation, energy, entropy, and inverse different moment, have been widely applied and achieved reasonably satisfactory results [Pacifici et al., 2009].”
Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014, background): “enriched input space (the spatial features only [2], [4], [16] or a”